Knowledge Modelling Product Manager - Contract - Remote in the UK Remote - candidates may work from anywhere in the UK Contract Market rate - via Umbrella Role Overview The client is seeking an experienced Knowledge Modelling Product Manager to support the successful adoption of a semantic abstraction layer across its central platform team and multiple business units. This role requires strong, hands-on knowledge of modelling expertise and the ability to bridge the gap between semantic technologies and the operational needs of teams that are new to ontology-based approaches. You will work closely with platform architects, engineers, data specialists and subject-matter experts to establish modelling standards, develop canonical domain models and build sustainable semantic-modelling capability across the organisation. You will work closely with: Data Portfolio Managers Semantic Platform Administrators Platform Architects and Engineers Data Modellers Data Engineers Subject-Matter Experts Business-unit stakeholders Key Responsibilities 1. Client Platform Team Enablement Train platform architects and engineers in semantic-modelling fundamentals, including OWL, RDF/RDFS, SKOS, SPARQL, graph-database operation, ontology-design patterns and common modelling pitfalls. Guide the engineering team in the implementation of ontology-management services, ensuring that technical decisions support the intended business outcomes. Establish semantic standards for the Client Platform, including naming conventions, annotation requirements, foundational ontology-alignment patterns and shared vocabularies. Work collaboratively with relevant architecture, data and governance teams to ensure consistent implementation of these standards. Provide expert guidance to technical teams without taking ownership of software engineering or platform-infrastructure delivery. 2. Business-Unit Enablement Work directly with subject-matter experts and data modellers across the organisation to develop their first canonical domain models. Facilitate structured workshops in which subject-matter experts articulate their domain knowledge and data modellers translate it into formal semantic-model decisions. Apply a hands-on and pragmatic approach rather than relying on theoretical training alone. Build capability progressively by initially working alongside teams, then coaching them and ultimately enabling them to operate independently. Develop reusable guidance materials, including: Modelling guides Worked examples based on real business domains Ontology-design patterns Decision frameworks for common modelling questions Help teams make practical decisions about model granularity, class hierarchies, properties, relationships and reuse. 3. Stakeholder Engagement and Adoption Explain the business value of the semantic layer to non-technical stakeholders using clear, outcome-focused language. Present tangible examples of how well-designed canonical models support business and technology outcomes. Address stakeholder concerns honestly, including where semantic approaches introduce additional effort and where that investment is expected to deliver value. Promote adoption across culturally and technically diverse stakeholder groups. Demonstrate how semantic modelling can improve: Data findability Interoperability Intellectual-property protection Cross-business data understanding Application-development Data-product descriptions Integration efficiency and cost 4. Modelling Quality Assurance Act as the expert reviewer within the model-publication process during the initial increments of the Client Platform. Review submitted models for: Structural quality Standards compliance Pattern adherence Reusability Interoperability readiness Define clear and practical criteria for what a high-quality canonical domain model looks like. Produce concrete examples that teams can use as reference models. Identify and challenge modelling anti-patterns before they become Embedded across the organisation. Ensure that data and governance policies are reflected correctly in the models, while recognising that policy ownership sits with the relevant governance teams. Essential Experience Significant hands-on ontology-development experience within an industrial, commercial or enterprise environment. Practical expertise in: Web Ontology Language - OWL Resource Description Framework - RDF RDF Schema - RDFS Simple Knowledge Organization System - SKOS SPARQL OWL API Experience designing, developing and maintaining enterprise semantic models or canonical domain models. Experience operating open-standards graph databases, including configuration, data loading, querying and performance considerations. Demonstrable ability to translate complex knowledge from subject-matter experts into formal semantic models. Experience introducing semantic technologies to teams with limited or no previous exposure to ontology-based approaches. Evidence of achieving successful adoption and capability transfer, rather than solely delivering technical artefacts. Experience facilitating requirements-gathering and domain-modelling workshops with technical and non-technical participants. Essential Skills Strong ontology-engineering and knowledge-modelling capability. Ability to explain semantic-modelling concepts to non-technical audiences without unnecessary jargon. Ability to work with specialist domain experts and extract the knowledge required to produce coherent, usable models. Strong requirements-analysis and stakeholder-management skills. Comfortable working in an environment where the technology and operating model are still developing. Pragmatic, patient and able to provide clarity in ambiguous situations. Strong views on modelling quality, balanced with the flexibility to respond to practical delivery constraints. Excellent written communication skills, with the ability to produce concise and usable guidance rather than academic documentation. Clear and straightforward verbal communication. Comfortable working across multidisciplinary and multicultural teams. Able to influence technical decisions without direct ownership of engineering delivery. Desirable Experience Broader graph-database experience. Requirements-life cycle management. Product-management or platform-product experience. Data-governance implementation. Enterprise data architecture. Team leadership, coaching or capability development. Experience working across multiple business units or federated organisations. Scope of the Role The role is intended to build lasting capability within the platform team and wider business. The objective is to move progressively from hands-on delivery to coaching and advisory support as internal teams become more autonomous. This is.*not a software-engineering role*. The contractor will not be responsible for building the underlying platform infrastructure but must have sufficient technical expertise to guide the teams responsible for its delivery. This is.*not a data-governance ownership role*. Governance policies will be owned by the appropriate governance stakeholders; this role will help ensure those policies are implemented effectively within semantic models and platform practices.
24/06/2026
Contractor
Knowledge Modelling Product Manager - Contract - Remote in the UK Remote - candidates may work from anywhere in the UK Contract Market rate - via Umbrella Role Overview The client is seeking an experienced Knowledge Modelling Product Manager to support the successful adoption of a semantic abstraction layer across its central platform team and multiple business units. This role requires strong, hands-on knowledge of modelling expertise and the ability to bridge the gap between semantic technologies and the operational needs of teams that are new to ontology-based approaches. You will work closely with platform architects, engineers, data specialists and subject-matter experts to establish modelling standards, develop canonical domain models and build sustainable semantic-modelling capability across the organisation. You will work closely with: Data Portfolio Managers Semantic Platform Administrators Platform Architects and Engineers Data Modellers Data Engineers Subject-Matter Experts Business-unit stakeholders Key Responsibilities 1. Client Platform Team Enablement Train platform architects and engineers in semantic-modelling fundamentals, including OWL, RDF/RDFS, SKOS, SPARQL, graph-database operation, ontology-design patterns and common modelling pitfalls. Guide the engineering team in the implementation of ontology-management services, ensuring that technical decisions support the intended business outcomes. Establish semantic standards for the Client Platform, including naming conventions, annotation requirements, foundational ontology-alignment patterns and shared vocabularies. Work collaboratively with relevant architecture, data and governance teams to ensure consistent implementation of these standards. Provide expert guidance to technical teams without taking ownership of software engineering or platform-infrastructure delivery. 2. Business-Unit Enablement Work directly with subject-matter experts and data modellers across the organisation to develop their first canonical domain models. Facilitate structured workshops in which subject-matter experts articulate their domain knowledge and data modellers translate it into formal semantic-model decisions. Apply a hands-on and pragmatic approach rather than relying on theoretical training alone. Build capability progressively by initially working alongside teams, then coaching them and ultimately enabling them to operate independently. Develop reusable guidance materials, including: Modelling guides Worked examples based on real business domains Ontology-design patterns Decision frameworks for common modelling questions Help teams make practical decisions about model granularity, class hierarchies, properties, relationships and reuse. 3. Stakeholder Engagement and Adoption Explain the business value of the semantic layer to non-technical stakeholders using clear, outcome-focused language. Present tangible examples of how well-designed canonical models support business and technology outcomes. Address stakeholder concerns honestly, including where semantic approaches introduce additional effort and where that investment is expected to deliver value. Promote adoption across culturally and technically diverse stakeholder groups. Demonstrate how semantic modelling can improve: Data findability Interoperability Intellectual-property protection Cross-business data understanding Application-development Data-product descriptions Integration efficiency and cost 4. Modelling Quality Assurance Act as the expert reviewer within the model-publication process during the initial increments of the Client Platform. Review submitted models for: Structural quality Standards compliance Pattern adherence Reusability Interoperability readiness Define clear and practical criteria for what a high-quality canonical domain model looks like. Produce concrete examples that teams can use as reference models. Identify and challenge modelling anti-patterns before they become Embedded across the organisation. Ensure that data and governance policies are reflected correctly in the models, while recognising that policy ownership sits with the relevant governance teams. Essential Experience Significant hands-on ontology-development experience within an industrial, commercial or enterprise environment. Practical expertise in: Web Ontology Language - OWL Resource Description Framework - RDF RDF Schema - RDFS Simple Knowledge Organization System - SKOS SPARQL OWL API Experience designing, developing and maintaining enterprise semantic models or canonical domain models. Experience operating open-standards graph databases, including configuration, data loading, querying and performance considerations. Demonstrable ability to translate complex knowledge from subject-matter experts into formal semantic models. Experience introducing semantic technologies to teams with limited or no previous exposure to ontology-based approaches. Evidence of achieving successful adoption and capability transfer, rather than solely delivering technical artefacts. Experience facilitating requirements-gathering and domain-modelling workshops with technical and non-technical participants. Essential Skills Strong ontology-engineering and knowledge-modelling capability. Ability to explain semantic-modelling concepts to non-technical audiences without unnecessary jargon. Ability to work with specialist domain experts and extract the knowledge required to produce coherent, usable models. Strong requirements-analysis and stakeholder-management skills. Comfortable working in an environment where the technology and operating model are still developing. Pragmatic, patient and able to provide clarity in ambiguous situations. Strong views on modelling quality, balanced with the flexibility to respond to practical delivery constraints. Excellent written communication skills, with the ability to produce concise and usable guidance rather than academic documentation. Clear and straightforward verbal communication. Comfortable working across multidisciplinary and multicultural teams. Able to influence technical decisions without direct ownership of engineering delivery. Desirable Experience Broader graph-database experience. Requirements-life cycle management. Product-management or platform-product experience. Data-governance implementation. Enterprise data architecture. Team leadership, coaching or capability development. Experience working across multiple business units or federated organisations. Scope of the Role The role is intended to build lasting capability within the platform team and wider business. The objective is to move progressively from hands-on delivery to coaching and advisory support as internal teams become more autonomous. This is.*not a software-engineering role*. The contractor will not be responsible for building the underlying platform infrastructure but must have sufficient technical expertise to guide the teams responsible for its delivery. This is.*not a data-governance ownership role*. Governance policies will be owned by the appropriate governance stakeholders; this role will help ensure those policies are implemented effectively within semantic models and platform practices.
Do you want to boost your career and collaborate with expert, talented colleagues to solve and deliver against our clients' most important challenges? We are growing and are looking for people to join our team. You'll be part of an entrepreneurial, high-growth environment of 300,000 employees. Our dynamic organization allows you to work across functional business pillars, contributing your ideas, experiences, diverse thinking, and a strong mindset. Are you ready? About your role Enterprise AI is forcing organisations to rethink their data estates. Data platforms designed mainly for reporting are often not enough for GenAI, semantic search, agentic workflows and AI-enabled decision making. Clients now need data that is trusted, governed, contextualised and consumable by both people and intelligent systems. We are looking for client-facing Enterprise Data Architects to join our growing Enterprise AI practice. You will help clients transform fragmented data estates into AI ready foundations, advising on architecture decisions across cloud data platforms, lakehouse and warehouse patterns, data products, semantic layers, metadata, lineage, governance, knowledge graphs and GenAI retrieval patterns. This is a consulting role, not a purely internal architecture role. You will diagnose ambiguous client problems, shape options, make trade offs explicit, and translate complex data architecture issues into clear decisions for both technical teams and executive stakeholders. You will work in cross functional teams alongside product owners, data scientists, ML and GenAI engineers, data engineers, business analysts and client stakeholders. Typical outputs may include target state architectures, maturity assessments, platform option appraisals, data product designs, governance models, lineage maps, ontology and semantic models, integration patterns, GenAI data readiness assessments and implementation roadmaps. We are hiring across several levels. At earlier levels, we expect strong architecture delivery experience and hands on platform understanding. At senior levels, we expect the ability to shape enterprise data strategy, influence senior stakeholders, lead complex architecture decisions and guide multi disciplinary delivery teams. We do not expect every candidate to be a specialist in every aspect of AI ready data architecture. We are looking for architects with strong core data architecture experience and credible depth in some of the areas that matter for AI enabled data estates, such as governance, semantic modelling, lakehouse architecture, data products, metadata management, knowledge graphs, RAG or enterprise data strategy. Responsibilities Design AI ready enterprise data architectures that enable analytics, AI, ML, GenAI and agentic applications to consume data accurately, securely and with appropriate business context. Assess clients' existing data estates, diagnose structural, governance, semantic and quality issues, and design pragmatic modernisation roadmaps. Advise clients on architecture and platform choices, helping them navigate trade offs between lakehouses, warehouses, data fabrics, graph databases, semantic layers, vector search and hybrid architectures. Define data governance and metadata patterns covering ownership, stewardship, quality, lineage, cataloguing, access control and data lifecycle management. Design data products, data contracts and information models that make enterprise data reusable across analytics, AI, GenAI and operational workflows. Shape semantic layers, ontologies and knowledge graph patterns where these improve data discoverability, interoperability, explainability or AI consumption. Oversee high level design of ingestion, integration and transformation patterns, including batch, event driven and real time architectures. Identify and mitigate data related risks, including poor data quality, weak provenance, data leakage, inappropriate access, retrieval failure and inference time use of enterprise knowledge. Act as a trusted advisor to client stakeholders, translating technical architecture concepts into clear business outcomes, options and risks. Contribute to proposals, client conversations, internal methods and thought leadership on enterprise data architecture and AI ready data foundations. Skills and Qualifications Essential Skills 5-10+ years, depending on level, in data architecture, enterprise architecture, solution architecture or senior data engineering roles. Demonstrable experience designing modern data architectures for analytics, AI, ML or GenAI consumption. Strong understanding of enterprise data architecture patterns, including cloud data platforms, lakehouses, warehouses, data integration, data modelling and metadata management. Experience contributing to or leading data governance initiatives, including catalogues, lineage, ownership, stewardship, data quality and metadata management. Practical understanding of semantic layers, ontologies or knowledge graph concepts, with hands on experience in at least one of these areas. Deep experience with at least one major cloud data platform, such as AWS, Azure or Google Cloud, and familiarity with leading lakehouse or warehouse technologies. Understanding of how data architecture decisions affect AI and GenAI outcomes, including data quality, provenance, context, retrieval, security, privacy and semantic consistency. Familiarity with GenAI data patterns such as retrieval augmented generation, vector search, embedding pipelines, chunking strategies or enterprise search. Strong stakeholder management and communication skills, with the ability to present complex technical trade offs clearly to non technical sponsors and senior executives. Excellent written and verbal communication skills in English. Bachelor's degree or equivalent experience; quantitative, technical or analytical disciplines are an advantage. Willingness to travel, up to around 60% depending on project requirements, across the UK and internationally. Preferred Skills A second major European language is an advantage. Experience with graph modelling, ontology standards or graph query languages such as RDF, OWL and SPARQL. Familiarity with feature store design and MLOps / DataOps pipeline integration. Experience with stream processing at scale using Apache Kafka or Apache Flink. Background in master data management or data mesh architecture. Consulting or comparable client facing delivery experience. Exposure to cloud data platforms (Databricks, Snowflake, Microsoft Fabric, Azure Synapse, Google BigQuery, Amazon Redshift), data engineering and orchestration (Spark, dbt, Airflow, Azure Data Factory, AWS Glue, Dataflow, Kafka, Flink), governance, catalogue and lineage tools (Microsoft Purview, Collibra, Informatica, Alation, Atlan, OpenLineage), graph, ontology and semantic technologies (Neo4j, Amazon Neptune, Stardog, GraphDB, RDF, OWL, SPARQL), or AI/ML data infrastructure (vector databases such as Pinecone, Weaviate, Milvus, Azure AI Search, OpenSearch or pgvector; feature stores such as Feast or Tecton; model lifecycle and experiment tracking tools such as MLflow). We do not expect candidates to have worked with all of them. Personal Attributes Comfortable working in ambiguous consulting environments, shaping options, making trade offs explicit and taking senior stakeholders on the journey from strategy to implementation. Self directed, able to prioritise and juggle multiple workstreams. Clear communicator who can simplify complexity for technical and non technical audiences alike. Collaborative, curious, continuous learner. We offer industry leading compensation and benefits, along with top training and development opportunities so that you can grow your career and achieve your personal ambitions.
22/06/2026
Full time
Do you want to boost your career and collaborate with expert, talented colleagues to solve and deliver against our clients' most important challenges? We are growing and are looking for people to join our team. You'll be part of an entrepreneurial, high-growth environment of 300,000 employees. Our dynamic organization allows you to work across functional business pillars, contributing your ideas, experiences, diverse thinking, and a strong mindset. Are you ready? About your role Enterprise AI is forcing organisations to rethink their data estates. Data platforms designed mainly for reporting are often not enough for GenAI, semantic search, agentic workflows and AI-enabled decision making. Clients now need data that is trusted, governed, contextualised and consumable by both people and intelligent systems. We are looking for client-facing Enterprise Data Architects to join our growing Enterprise AI practice. You will help clients transform fragmented data estates into AI ready foundations, advising on architecture decisions across cloud data platforms, lakehouse and warehouse patterns, data products, semantic layers, metadata, lineage, governance, knowledge graphs and GenAI retrieval patterns. This is a consulting role, not a purely internal architecture role. You will diagnose ambiguous client problems, shape options, make trade offs explicit, and translate complex data architecture issues into clear decisions for both technical teams and executive stakeholders. You will work in cross functional teams alongside product owners, data scientists, ML and GenAI engineers, data engineers, business analysts and client stakeholders. Typical outputs may include target state architectures, maturity assessments, platform option appraisals, data product designs, governance models, lineage maps, ontology and semantic models, integration patterns, GenAI data readiness assessments and implementation roadmaps. We are hiring across several levels. At earlier levels, we expect strong architecture delivery experience and hands on platform understanding. At senior levels, we expect the ability to shape enterprise data strategy, influence senior stakeholders, lead complex architecture decisions and guide multi disciplinary delivery teams. We do not expect every candidate to be a specialist in every aspect of AI ready data architecture. We are looking for architects with strong core data architecture experience and credible depth in some of the areas that matter for AI enabled data estates, such as governance, semantic modelling, lakehouse architecture, data products, metadata management, knowledge graphs, RAG or enterprise data strategy. Responsibilities Design AI ready enterprise data architectures that enable analytics, AI, ML, GenAI and agentic applications to consume data accurately, securely and with appropriate business context. Assess clients' existing data estates, diagnose structural, governance, semantic and quality issues, and design pragmatic modernisation roadmaps. Advise clients on architecture and platform choices, helping them navigate trade offs between lakehouses, warehouses, data fabrics, graph databases, semantic layers, vector search and hybrid architectures. Define data governance and metadata patterns covering ownership, stewardship, quality, lineage, cataloguing, access control and data lifecycle management. Design data products, data contracts and information models that make enterprise data reusable across analytics, AI, GenAI and operational workflows. Shape semantic layers, ontologies and knowledge graph patterns where these improve data discoverability, interoperability, explainability or AI consumption. Oversee high level design of ingestion, integration and transformation patterns, including batch, event driven and real time architectures. Identify and mitigate data related risks, including poor data quality, weak provenance, data leakage, inappropriate access, retrieval failure and inference time use of enterprise knowledge. Act as a trusted advisor to client stakeholders, translating technical architecture concepts into clear business outcomes, options and risks. Contribute to proposals, client conversations, internal methods and thought leadership on enterprise data architecture and AI ready data foundations. Skills and Qualifications Essential Skills 5-10+ years, depending on level, in data architecture, enterprise architecture, solution architecture or senior data engineering roles. Demonstrable experience designing modern data architectures for analytics, AI, ML or GenAI consumption. Strong understanding of enterprise data architecture patterns, including cloud data platforms, lakehouses, warehouses, data integration, data modelling and metadata management. Experience contributing to or leading data governance initiatives, including catalogues, lineage, ownership, stewardship, data quality and metadata management. Practical understanding of semantic layers, ontologies or knowledge graph concepts, with hands on experience in at least one of these areas. Deep experience with at least one major cloud data platform, such as AWS, Azure or Google Cloud, and familiarity with leading lakehouse or warehouse technologies. Understanding of how data architecture decisions affect AI and GenAI outcomes, including data quality, provenance, context, retrieval, security, privacy and semantic consistency. Familiarity with GenAI data patterns such as retrieval augmented generation, vector search, embedding pipelines, chunking strategies or enterprise search. Strong stakeholder management and communication skills, with the ability to present complex technical trade offs clearly to non technical sponsors and senior executives. Excellent written and verbal communication skills in English. Bachelor's degree or equivalent experience; quantitative, technical or analytical disciplines are an advantage. Willingness to travel, up to around 60% depending on project requirements, across the UK and internationally. Preferred Skills A second major European language is an advantage. Experience with graph modelling, ontology standards or graph query languages such as RDF, OWL and SPARQL. Familiarity with feature store design and MLOps / DataOps pipeline integration. Experience with stream processing at scale using Apache Kafka or Apache Flink. Background in master data management or data mesh architecture. Consulting or comparable client facing delivery experience. Exposure to cloud data platforms (Databricks, Snowflake, Microsoft Fabric, Azure Synapse, Google BigQuery, Amazon Redshift), data engineering and orchestration (Spark, dbt, Airflow, Azure Data Factory, AWS Glue, Dataflow, Kafka, Flink), governance, catalogue and lineage tools (Microsoft Purview, Collibra, Informatica, Alation, Atlan, OpenLineage), graph, ontology and semantic technologies (Neo4j, Amazon Neptune, Stardog, GraphDB, RDF, OWL, SPARQL), or AI/ML data infrastructure (vector databases such as Pinecone, Weaviate, Milvus, Azure AI Search, OpenSearch or pgvector; feature stores such as Feast or Tecton; model lifecycle and experiment tracking tools such as MLflow). We do not expect candidates to have worked with all of them. Personal Attributes Comfortable working in ambiguous consulting environments, shaping options, making trade offs explicit and taking senior stakeholders on the journey from strategy to implementation. Self directed, able to prioritise and juggle multiple workstreams. Clear communicator who can simplify complexity for technical and non technical audiences alike. Collaborative, curious, continuous learner. We offer industry leading compensation and benefits, along with top training and development opportunities so that you can grow your career and achieve your personal ambitions.
Role : Ontology Engineer Location: UK Type: Contract Essential Skills: Proven experience in ontology engineering with hands-on OWL, RDF and SKOS delivery in a production or client-facing environment specific semantic ontology and taxonomy experience particularly using tools (Protege and Graphview) Ability to extend and refine existing ontologies, not just build from scratch Strong SPARQL capability including validation and reasoning queries Experience owning and governing taxonomies as versioned, change-controlled assets Understanding of how ontologies and taxonomies feed into AI and RAG systems Strong documentation discipline and ability to record modelling rationale clearly Experience collaborating with technical teams including data engineers and AI developers Highly Desirable: Experience with Protege, TopBraid or equivalent ontology tooling Familiarity with SHACL for constraint validation Background in financial services, private equity or similarly structured enterprise environments Understanding of knowledge grounding principles for large language models Experience with linked data architectures and triple store platforms The Context: Thebes Group is an optimisation company specialising in AI-enabled transformation. We help organisations improve workflow, reporting, information management, and operational decision-making by combining process optimisation, knowledge architecture, semantic technologies, automation, and artificial intelligence. We are currently delivering an AI transformation programme for a private equity group, focused on enhancing group-level operations through intelligent workflows, improved information accessibility, executive reporting, and AI-driven operational intelligence. A foundation ontology and taxonomy already exists. The data is mapped, manageable in scope, and well understood within the team. This is not a build-from-scratch engagement. A Knowledge Graph Architect sits alongside this role to make the semantic structures operational, and an AI engineer handles the agent build. The Ontology Engineer owns the meaning layer: what data is, what the relationships between concepts are, and what the rules are that govern how information should be understood across the organisation. The Role: As Ontology Engineer, you are responsible for the semantic foundation of the programme. You will take the existing ontology and taxonomy and expand, refine and govern them as operational requirements evolve and new agent use cases emerge. Your work defines what the organisation's data means and how concepts relate to each other. That meaning is the input everything else depends on: the knowledge graph, the data pipelines, and ultimately the accuracy of what AI agents know and how they reason. This is a precision role. The quality of your semantic models directly determines the quality of agent outputs across the group. What You Will Do: Expand and maintain the existing ontology as new business requirements emerge, ensuring consistency with the established semantic model Define and refine classes, subclasses, properties, relationships and business rules that accurately represent group-level operational concepts Develop and govern OWL, RDF, RDFS and SKOS artefacts that form the semantic foundation of the programme Own the enterprise taxonomy, managing it as a versioned, governed asset with clear change control and documented rationale Create and maintain SPARQL queries to validate the integrity and consistency of the ontology Work closely with the Knowledge Graph Architect to ensure semantic models translate correctly into graph structures Collaborate with the AI engineer to review agent outputs, identify where knowledge-layer gaps are causing errors, and refine the ontology accordingly Establish and maintain semantic standards and governance frameworks covering versioning, change management and stewardship Document all modelling decisions and change history to support long-term knowledge asset governance Full Technical Skills: Core Semantic Technologies Ontology Languages Query & Validation Reasoning & Logic OWL 2 (DL, EL, RL profiles) RDF/RDFS SKOS SHACL OWL Manchester Syntax Turtle/N-Triples/JSON-LD SPARQL 1.1 SHACL constraint authoring SPARQL reasoning queries Ontology validation tooling Shape expressions (ShEx) Description Logic OWL reasoning (HermiT, Pellet, FaCT ) Inference rule design Formal concept analysis Subsumption reasoning Tooling & Platforms Ontology Editors Triple Stores Version Control Protege TopBraid Composer PoolParty Semaphore VocBench Apache Jena/Fuseki Stardog GraphDB (Ontotext) Amazon Neptune Virtuoso Git-based ontology versioning ROBOT (ontology build tool) Ontology diff tooling CI/CD for ontology pipelines Change log governance Knowledge Architecture Taxonomy & Classification Semantic Modelling AI & Knowledge Systems Taxonomy design and governance Faceted classification Controlled vocabularies Thesaurus construction ISO 25964 standards Domain modelling Concept modelling Entity-relationship design Metadata schema design Linked data principles RAG knowledge layer design Knowledge grounding for LLMs Ontology-driven agent design GraphRAG semantic integration Semantic retrieval patterns Scope and Boundary: This engagement covers group-level operations only. Fund management, investment decision-making and fund-level data are explicitly out of scope. The data environment is manageable in scale and well understood within the delivery team. You will not be working in isolation: the Knowledge Graph Architect, AI engineer and wider team provide context, technical partnership and support. Why Thebes Group: This role offers technically precise, high-impact ontology work on a live AI programme where the semantic layer you build and govern directly determines what agents know and how accurately they perform. You will work within a structured delivery team, reporting into Thebes Group leadership, with clear accountability and real operational stakes.
18/06/2026
Contractor
Role : Ontology Engineer Location: UK Type: Contract Essential Skills: Proven experience in ontology engineering with hands-on OWL, RDF and SKOS delivery in a production or client-facing environment specific semantic ontology and taxonomy experience particularly using tools (Protege and Graphview) Ability to extend and refine existing ontologies, not just build from scratch Strong SPARQL capability including validation and reasoning queries Experience owning and governing taxonomies as versioned, change-controlled assets Understanding of how ontologies and taxonomies feed into AI and RAG systems Strong documentation discipline and ability to record modelling rationale clearly Experience collaborating with technical teams including data engineers and AI developers Highly Desirable: Experience with Protege, TopBraid or equivalent ontology tooling Familiarity with SHACL for constraint validation Background in financial services, private equity or similarly structured enterprise environments Understanding of knowledge grounding principles for large language models Experience with linked data architectures and triple store platforms The Context: Thebes Group is an optimisation company specialising in AI-enabled transformation. We help organisations improve workflow, reporting, information management, and operational decision-making by combining process optimisation, knowledge architecture, semantic technologies, automation, and artificial intelligence. We are currently delivering an AI transformation programme for a private equity group, focused on enhancing group-level operations through intelligent workflows, improved information accessibility, executive reporting, and AI-driven operational intelligence. A foundation ontology and taxonomy already exists. The data is mapped, manageable in scope, and well understood within the team. This is not a build-from-scratch engagement. A Knowledge Graph Architect sits alongside this role to make the semantic structures operational, and an AI engineer handles the agent build. The Ontology Engineer owns the meaning layer: what data is, what the relationships between concepts are, and what the rules are that govern how information should be understood across the organisation. The Role: As Ontology Engineer, you are responsible for the semantic foundation of the programme. You will take the existing ontology and taxonomy and expand, refine and govern them as operational requirements evolve and new agent use cases emerge. Your work defines what the organisation's data means and how concepts relate to each other. That meaning is the input everything else depends on: the knowledge graph, the data pipelines, and ultimately the accuracy of what AI agents know and how they reason. This is a precision role. The quality of your semantic models directly determines the quality of agent outputs across the group. What You Will Do: Expand and maintain the existing ontology as new business requirements emerge, ensuring consistency with the established semantic model Define and refine classes, subclasses, properties, relationships and business rules that accurately represent group-level operational concepts Develop and govern OWL, RDF, RDFS and SKOS artefacts that form the semantic foundation of the programme Own the enterprise taxonomy, managing it as a versioned, governed asset with clear change control and documented rationale Create and maintain SPARQL queries to validate the integrity and consistency of the ontology Work closely with the Knowledge Graph Architect to ensure semantic models translate correctly into graph structures Collaborate with the AI engineer to review agent outputs, identify where knowledge-layer gaps are causing errors, and refine the ontology accordingly Establish and maintain semantic standards and governance frameworks covering versioning, change management and stewardship Document all modelling decisions and change history to support long-term knowledge asset governance Full Technical Skills: Core Semantic Technologies Ontology Languages Query & Validation Reasoning & Logic OWL 2 (DL, EL, RL profiles) RDF/RDFS SKOS SHACL OWL Manchester Syntax Turtle/N-Triples/JSON-LD SPARQL 1.1 SHACL constraint authoring SPARQL reasoning queries Ontology validation tooling Shape expressions (ShEx) Description Logic OWL reasoning (HermiT, Pellet, FaCT ) Inference rule design Formal concept analysis Subsumption reasoning Tooling & Platforms Ontology Editors Triple Stores Version Control Protege TopBraid Composer PoolParty Semaphore VocBench Apache Jena/Fuseki Stardog GraphDB (Ontotext) Amazon Neptune Virtuoso Git-based ontology versioning ROBOT (ontology build tool) Ontology diff tooling CI/CD for ontology pipelines Change log governance Knowledge Architecture Taxonomy & Classification Semantic Modelling AI & Knowledge Systems Taxonomy design and governance Faceted classification Controlled vocabularies Thesaurus construction ISO 25964 standards Domain modelling Concept modelling Entity-relationship design Metadata schema design Linked data principles RAG knowledge layer design Knowledge grounding for LLMs Ontology-driven agent design GraphRAG semantic integration Semantic retrieval patterns Scope and Boundary: This engagement covers group-level operations only. Fund management, investment decision-making and fund-level data are explicitly out of scope. The data environment is manageable in scale and well understood within the delivery team. You will not be working in isolation: the Knowledge Graph Architect, AI engineer and wider team provide context, technical partnership and support. Why Thebes Group: This role offers technically precise, high-impact ontology work on a live AI programme where the semantic layer you build and govern directly determines what agents know and how accurately they perform. You will work within a structured delivery team, reporting into Thebes Group leadership, with clear accountability and real operational stakes.
Career Choices Dewis Gyrfa Ltd
Manchester, Lancashire
Career Choices Dewis Gyrfa Ltd is seeking a Knowledge Graph and Ontology Specialist to play a leading role in designing and delivering semantic data models and knowledge graphs. The role involves collaboration with multidisciplinary teams to improve information structuring and retrieval across NICE's digital platforms. Join us to help shape the future of health and care decision-making while enjoying flexible working arrangements and a supportive work environment reflecting inclusion and community values.
17/06/2026
Full time
Career Choices Dewis Gyrfa Ltd is seeking a Knowledge Graph and Ontology Specialist to play a leading role in designing and delivering semantic data models and knowledge graphs. The role involves collaboration with multidisciplinary teams to improve information structuring and retrieval across NICE's digital platforms. Join us to help shape the future of health and care decision-making while enjoying flexible working arrangements and a supportive work environment reflecting inclusion and community values.
Career Choices Dewis Gyrfa Ltd
Manchester, Lancashire
NICE - National Institute for Health and Care Excellence Location: Manchester, M1 3BN Pay: Contract Type: Contract Hours: Disability Confident: No Closing Date: 27/06/2026 About this job A Vacancy at NICE The National Institute for Health and Care Excellence. Do you want to do meaningful work that makes a genuine difference to society? Our main purpose here at The National Institute for Health and Care Excellence (NICE) is to improve health and wellbeing by putting science and evidence at the heart of health and care decision making. As an organisation we all collaborate to achieve this goal by empowering our workforce to do great things. Please note that this role may not be eligible for sponsorship under the Skilled Worker route. Please refer to the DirectGov website for more information on eligibility. We reserve the right to close the advert early should we receive sufficient applications, so please don't delay your submission. The Knowledge Graph and Ontology Specialist will play a leading role in designing and delivering semantic data models, ontologies, and knowledge graphs that underpin NICE's digital products and information services. Working closely with multidisciplinary teams, the role focuses on improving how information is structured, linked, discovered, and reused-supporting intelligent search, interoperability, and future ready digital services for both internal and external users. What you will do / bring to the role Lead the design, development and implementation of ontologies, taxonomies, metadata schemas and knowledge graphs, embedding them into NICE systems and services. Develop semantic search and retrieval capabilities, helping users find information more effectively across NICE digital platforms. Provide advice on semantic technologies, information modelling, interoperability, and data integration. Promote structured, interoperable information and influence stakeholders across programmes. Collaborate with architects, engineers, analysts and partners to shape semantic solutions and roadmaps. Apply strong expertise in semantic web technologies and standards (such as RDF, OWL, SPARQL and SHACL) alongside knowledge of graph databases and data integration practices. Communicate complex technical concepts clearly and confidently to a wide range of technical and non technical stakeholders, influencing decisions and driving adoption. The Architecture & Data team sits at the heart of NICE's digital transformation, setting the direction for how data, analytics and architecture are used across the organisation. As a centre of excellence, the team defines standards, embeds strong data governance, and enables high quality, impactful delivery. Working closely with teams across NICE, it supports responsible innovation and ensures data is used effectively to improve health and care outcomes. We are passionate and proud of the work we do and the impact we make. NICE offer Secure your future with one of the most rewarding pension schemes in the UK. Enjoy a healthy work life balance with options like remote working, compressed hours and flexible start/finish times. Save on shopping, dining and more with a Blue Light Card. Time to recharge: start with 27 days' annual leave plus bank holidays. Inclusive staff networks. Join supportive communities like Women in NICE, Race Equality Network, Disability Advocacy and NICE and Proud. Tailored development. Grow your career with personalised learning and development opportunities. This advert closes on Thursday 11 Jun 2026.
17/06/2026
Full time
NICE - National Institute for Health and Care Excellence Location: Manchester, M1 3BN Pay: Contract Type: Contract Hours: Disability Confident: No Closing Date: 27/06/2026 About this job A Vacancy at NICE The National Institute for Health and Care Excellence. Do you want to do meaningful work that makes a genuine difference to society? Our main purpose here at The National Institute for Health and Care Excellence (NICE) is to improve health and wellbeing by putting science and evidence at the heart of health and care decision making. As an organisation we all collaborate to achieve this goal by empowering our workforce to do great things. Please note that this role may not be eligible for sponsorship under the Skilled Worker route. Please refer to the DirectGov website for more information on eligibility. We reserve the right to close the advert early should we receive sufficient applications, so please don't delay your submission. The Knowledge Graph and Ontology Specialist will play a leading role in designing and delivering semantic data models, ontologies, and knowledge graphs that underpin NICE's digital products and information services. Working closely with multidisciplinary teams, the role focuses on improving how information is structured, linked, discovered, and reused-supporting intelligent search, interoperability, and future ready digital services for both internal and external users. What you will do / bring to the role Lead the design, development and implementation of ontologies, taxonomies, metadata schemas and knowledge graphs, embedding them into NICE systems and services. Develop semantic search and retrieval capabilities, helping users find information more effectively across NICE digital platforms. Provide advice on semantic technologies, information modelling, interoperability, and data integration. Promote structured, interoperable information and influence stakeholders across programmes. Collaborate with architects, engineers, analysts and partners to shape semantic solutions and roadmaps. Apply strong expertise in semantic web technologies and standards (such as RDF, OWL, SPARQL and SHACL) alongside knowledge of graph databases and data integration practices. Communicate complex technical concepts clearly and confidently to a wide range of technical and non technical stakeholders, influencing decisions and driving adoption. The Architecture & Data team sits at the heart of NICE's digital transformation, setting the direction for how data, analytics and architecture are used across the organisation. As a centre of excellence, the team defines standards, embeds strong data governance, and enables high quality, impactful delivery. Working closely with teams across NICE, it supports responsible innovation and ensures data is used effectively to improve health and care outcomes. We are passionate and proud of the work we do and the impact we make. NICE offer Secure your future with one of the most rewarding pension schemes in the UK. Enjoy a healthy work life balance with options like remote working, compressed hours and flexible start/finish times. Save on shopping, dining and more with a Blue Light Card. Time to recharge: start with 27 days' annual leave plus bank holidays. Inclusive staff networks. Join supportive communities like Women in NICE, Race Equality Network, Disability Advocacy and NICE and Proud. Tailored development. Grow your career with personalised learning and development opportunities. This advert closes on Thursday 11 Jun 2026.
Adecco is an employment consultancy. We put expertise, energy, and enthusiasm into improving everyone's chance of being part of the workplace. We respect and appreciate people of all ethnicities, generations, religious beliefs, sexual orientations, gender identities, and more. We do this by showcasing their talents, skills, and unique experience in an inclusive environment that helps them thrive. Are you passionate about transforming enterprise data into meaningful insights? Do you thrive in innovative environments where you can shape the future of data architecture? If so, our client is looking for you! Join us as a Semantic Graph & Ontology Architect and play a pivotal role in developing a Smart Data Fabric that unifies various data sources like Snowflake, SharePoint, and ERP systems, all while enhancing AI capabilities through a sophisticated semantic, graph-native foundation. Role: Semantic Graph & Ontology Architect Duration: 6 Months (extension options) Location: Fully Remote Rate: Competitive (outside ir35) How You'll Make an Impact: As a hands-on leader, you will: Graph & Semantic Architecture: Design scalable graph schemas (LPG and/or RDF/OWL) to meet semantic and inference requirements. Author and optimise queries using Cypher, Gremlin, and SPARQL for seamless data traversal and reasoning. Define canonical entity models and mapping layers to integrate diverse data sources. Ontology Engineering & Reasoning: Create and maintain formal ontologies and taxonomies while governing their versioning and lifecycle. Implement logical inference for agent decision-making and ensure workflow integrity. Establish standards for semantic consistency and data quality checks. Hybrid Semantic Layer (Graph + Logic): Design a hybrid semantic layer that combines graph context with business logic for enhanced search and knowledge contextualization. Model RACI/RBAC as graph edges/nodes, embedding compliance rules for auditability. APIs, Patterns & Collaboration: Define clean API layers for semantic enrichment and retrieval; deliver reference implementations. Collaborate with platform engineers for agent connectivity and tool discovery patterns. Partner with data, platform, and security teams for governance and observability. Quality, Performance & Governance: Set performance budgets to ensure efficient query execution and prevent issues. Establish lineage and governance artefacts like semantic catalogues and audit trails. Document standards and mentor engineers in adopting graph and semantic patterns. What You Bring: A bachelor's or master's degree in computer science, Data Science, Mathematics, Engineering, or a related field. 7-12 years of experience in graph databases, semantic modelling, and ontology engineering. Expertise in query languages like Cypher, Gremlin, and SPARQL, with a strong understanding of LPG vs RDF/OWL tradeoffs. Hands-on experience with Neo4j, AWS Neptune, TigerGraph, or Stardog in a production environment. Proficiency in mapping enterprise data (Snowflake, MongoDB, SharePoint, ERP) into graph and ontology layers. A solid grasp of RBAC/RACI, data governance, lineage, and security controls. Ability to design clean APIs for semantic enrichment and retrieval. Familiarity with AWS services (IAM, VPC, S3, EKS/ECS/Lambda) in collaboration with platform teams. Preferred Qualifications: Experience with ontology tooling (Prot g , SHACL/SWRL) and reasoning engines. Prior delivery of enterprise knowledge graphs supporting workflows and audit trails. Exposure to vector retrieval and how graph context informs data re-ranking. Knowledge of observability tools like OpenTelemetry, Prometheus, and Grafana. Why Join Us? This is your opportunity to be at the forefront of data innovation in the energy sector! If you are eager to make a significant impact and collaborate with talented professionals, we want to hear from you! Apply now and embark on a journey to redefine how data drives decision-making in our client's organisation. Let's build a smarter future together! How to Apply: If you're excited about this opportunity and believe you're a great fit, please answer screening questions during application and submit your CV. Join our client and help shape the future of data engineering! We can't wait to welcome you aboard! Candidates will ideally show evidence of the above in their CV to be considered. Please be advised if you haven't heard from us within 48 hours then unfortunately your application has not been successful on this occasion, we may however keep your details on file for any suitable future vacancies and contact you accordingly. We use generative AI tools to support our candidate screening process. This helps us ensure a fair, consistent, and efficient experience for all applicants. Rest assured, all final decisions are made by our hiring team, and your application will be reviewed with care and attention.
12/06/2026
Contractor
Adecco is an employment consultancy. We put expertise, energy, and enthusiasm into improving everyone's chance of being part of the workplace. We respect and appreciate people of all ethnicities, generations, religious beliefs, sexual orientations, gender identities, and more. We do this by showcasing their talents, skills, and unique experience in an inclusive environment that helps them thrive. Are you passionate about transforming enterprise data into meaningful insights? Do you thrive in innovative environments where you can shape the future of data architecture? If so, our client is looking for you! Join us as a Semantic Graph & Ontology Architect and play a pivotal role in developing a Smart Data Fabric that unifies various data sources like Snowflake, SharePoint, and ERP systems, all while enhancing AI capabilities through a sophisticated semantic, graph-native foundation. Role: Semantic Graph & Ontology Architect Duration: 6 Months (extension options) Location: Fully Remote Rate: Competitive (outside ir35) How You'll Make an Impact: As a hands-on leader, you will: Graph & Semantic Architecture: Design scalable graph schemas (LPG and/or RDF/OWL) to meet semantic and inference requirements. Author and optimise queries using Cypher, Gremlin, and SPARQL for seamless data traversal and reasoning. Define canonical entity models and mapping layers to integrate diverse data sources. Ontology Engineering & Reasoning: Create and maintain formal ontologies and taxonomies while governing their versioning and lifecycle. Implement logical inference for agent decision-making and ensure workflow integrity. Establish standards for semantic consistency and data quality checks. Hybrid Semantic Layer (Graph + Logic): Design a hybrid semantic layer that combines graph context with business logic for enhanced search and knowledge contextualization. Model RACI/RBAC as graph edges/nodes, embedding compliance rules for auditability. APIs, Patterns & Collaboration: Define clean API layers for semantic enrichment and retrieval; deliver reference implementations. Collaborate with platform engineers for agent connectivity and tool discovery patterns. Partner with data, platform, and security teams for governance and observability. Quality, Performance & Governance: Set performance budgets to ensure efficient query execution and prevent issues. Establish lineage and governance artefacts like semantic catalogues and audit trails. Document standards and mentor engineers in adopting graph and semantic patterns. What You Bring: A bachelor's or master's degree in computer science, Data Science, Mathematics, Engineering, or a related field. 7-12 years of experience in graph databases, semantic modelling, and ontology engineering. Expertise in query languages like Cypher, Gremlin, and SPARQL, with a strong understanding of LPG vs RDF/OWL tradeoffs. Hands-on experience with Neo4j, AWS Neptune, TigerGraph, or Stardog in a production environment. Proficiency in mapping enterprise data (Snowflake, MongoDB, SharePoint, ERP) into graph and ontology layers. A solid grasp of RBAC/RACI, data governance, lineage, and security controls. Ability to design clean APIs for semantic enrichment and retrieval. Familiarity with AWS services (IAM, VPC, S3, EKS/ECS/Lambda) in collaboration with platform teams. Preferred Qualifications: Experience with ontology tooling (Prot g , SHACL/SWRL) and reasoning engines. Prior delivery of enterprise knowledge graphs supporting workflows and audit trails. Exposure to vector retrieval and how graph context informs data re-ranking. Knowledge of observability tools like OpenTelemetry, Prometheus, and Grafana. Why Join Us? This is your opportunity to be at the forefront of data innovation in the energy sector! If you are eager to make a significant impact and collaborate with talented professionals, we want to hear from you! Apply now and embark on a journey to redefine how data drives decision-making in our client's organisation. Let's build a smarter future together! How to Apply: If you're excited about this opportunity and believe you're a great fit, please answer screening questions during application and submit your CV. Join our client and help shape the future of data engineering! We can't wait to welcome you aboard! Candidates will ideally show evidence of the above in their CV to be considered. Please be advised if you haven't heard from us within 48 hours then unfortunately your application has not been successful on this occasion, we may however keep your details on file for any suitable future vacancies and contact you accordingly. We use generative AI tools to support our candidate screening process. This helps us ensure a fair, consistent, and efficient experience for all applicants. Rest assured, all final decisions are made by our hiring team, and your application will be reviewed with care and attention.
Semantic Architect Location: London, Hybrid Mission We are a growing health technology company building AI systems that improve how information flows within healthcare organisations. Our platform combines natural language processing, knowledge graphs, and generative AI to help healthcare providers and payers reduce administrative workload, improve decision making, and deliver better outcomes. Role Overview This is a senior, hands on technical leadership role focused on building production grade AI systems that combine LLMs, NLP pipelines, and structured knowledge. What You Will Do Your goal is to design and own the architecture that connects NLP pipelines processing clinical text, knowledge graphs and ontologies, LLM reasoning and orchestration layers, and evaluation and benchmarking systems. You'll also help scale the applied AI function by setting technical standards and coordinating complex AI development across engineering teams. Key Responsibilities Design end to end AI architectures integrating NLP, LLM orchestration, and knowledge graphs. Define how structured semantics guide and validate generative outputs. Set technical design standards for applied AI systems. Ensure AI features are robust, production ready, and aligned with product goals. Break product requirements into clear technical implementation plans. Coordinate work across NLP, graph engineering, and product teams. Maintain architectural coherence as systems scale. Design evaluation frameworks for hallucination detection, clinical concept extraction accuracy, model regression testing. Implement structured outputs and schema constrained generation. Introduce human in the loop review and continuous evaluation. Build AI workflows with traceability and auditability by default. Ensure systems align with healthcare regulatory requirements across UK and US contexts. Requirements MSc in Computer Science, AI, or related field (or equivalent experience). 3+ years building production AI systems involving structured knowledge. Strong experience integrating LLMs with knowledge graphs or structured data. Experience building NLP pipelines and semantic reasoning systems. Python for NLP pipelines, orchestration, and evaluation tooling. Experience with LLM engineering and prompt design using structured outputs. Familiarity with schema constrained generation (JSON / ontology driven outputs). Experience designing evaluation frameworks for production LLM systems. Experience designing AI systems grounded or validated by graph structures. Ability to collaborate with knowledge engineers on ontology design. Understanding of graph performance and scaling considerations. Experience in regulated environments (healthcare, fintech, gov, etc.) - Nice to have. Experience integrating AI systems into production services - Nice to have. Interest in building reliable AI systems in high impact domains - Nice to have. What We're Looking For Systems thinker who values clarity and architectural coherence. Pragmatic engineer focused on production impact. Comfortable taking ownership of complex technical systems. Strong communicator who shares and documents architectural knowledge. Hiring Process Introductory screening interview (30 minutes) Technical deep dive interview with AI and engineering leadership Final interview and offer. Benefits Competitive salary. Company pension. 25 days annual leave. Flexible hybrid working. Employee Assistance Programme. Central London office. Company Enigma
09/06/2026
Full time
Semantic Architect Location: London, Hybrid Mission We are a growing health technology company building AI systems that improve how information flows within healthcare organisations. Our platform combines natural language processing, knowledge graphs, and generative AI to help healthcare providers and payers reduce administrative workload, improve decision making, and deliver better outcomes. Role Overview This is a senior, hands on technical leadership role focused on building production grade AI systems that combine LLMs, NLP pipelines, and structured knowledge. What You Will Do Your goal is to design and own the architecture that connects NLP pipelines processing clinical text, knowledge graphs and ontologies, LLM reasoning and orchestration layers, and evaluation and benchmarking systems. You'll also help scale the applied AI function by setting technical standards and coordinating complex AI development across engineering teams. Key Responsibilities Design end to end AI architectures integrating NLP, LLM orchestration, and knowledge graphs. Define how structured semantics guide and validate generative outputs. Set technical design standards for applied AI systems. Ensure AI features are robust, production ready, and aligned with product goals. Break product requirements into clear technical implementation plans. Coordinate work across NLP, graph engineering, and product teams. Maintain architectural coherence as systems scale. Design evaluation frameworks for hallucination detection, clinical concept extraction accuracy, model regression testing. Implement structured outputs and schema constrained generation. Introduce human in the loop review and continuous evaluation. Build AI workflows with traceability and auditability by default. Ensure systems align with healthcare regulatory requirements across UK and US contexts. Requirements MSc in Computer Science, AI, or related field (or equivalent experience). 3+ years building production AI systems involving structured knowledge. Strong experience integrating LLMs with knowledge graphs or structured data. Experience building NLP pipelines and semantic reasoning systems. Python for NLP pipelines, orchestration, and evaluation tooling. Experience with LLM engineering and prompt design using structured outputs. Familiarity with schema constrained generation (JSON / ontology driven outputs). Experience designing evaluation frameworks for production LLM systems. Experience designing AI systems grounded or validated by graph structures. Ability to collaborate with knowledge engineers on ontology design. Understanding of graph performance and scaling considerations. Experience in regulated environments (healthcare, fintech, gov, etc.) - Nice to have. Experience integrating AI systems into production services - Nice to have. Interest in building reliable AI systems in high impact domains - Nice to have. What We're Looking For Systems thinker who values clarity and architectural coherence. Pragmatic engineer focused on production impact. Comfortable taking ownership of complex technical systems. Strong communicator who shares and documents architectural knowledge. Hiring Process Introductory screening interview (30 minutes) Technical deep dive interview with AI and engineering leadership Final interview and offer. Benefits Competitive salary. Company pension. 25 days annual leave. Flexible hybrid working. Employee Assistance Programme. Central London office. Company Enigma
Blue Planet Data Management LeadPostulerlocations: London: UK- Reading-Regustime type: Full timeposted on: Publié aujourd'huijob requisition id: R031020As the global leader in high-speed connectivity, Ciena is committed to a people-first approach. Our teams enjoy a culture focused on prioritizing a flexible work environment that empowers individual growth, well-being, and belonging. We're a technology company that leads with our humanity-driving our business priorities alongside meaningful social, community, and societal impact.Ciena is advancing intelligent, automated networks through its Blue Planet portfolio by enabling data-driven innovation and AI-powered operations. This role leads the definition of data strategy, architecture, and productization for telecom data, enabling scalable digital twin capabilities and data platforms that support next-generation network operations. The position plays a critical role in aligning data foundations with AI, automation, and product innovation across the portfolio. How you will make an impact: Define and own the canonical telco data model and ontology across network topology, service lifecycle, inventory, assurance, and OSS domains Align data models with industry standards including TM Forum Open APIs, SID/eTOM, Open Digital Architecture, YANG/NETCONF, and TOSCA Drive adoption of ontology models across product teams as the semantic foundation for data exchange, AI training, and digital twin deployment Develop and lead the data fabric product architecture, including federated access, streaming pipelines, virtualization, metadata management, and lineage tracking Translate data architecture vision into scalable, cloud-native and microservices-based implementations in collaboration with engineering teams Define and deliver the telco digital twin strategy, including use cases such as network optimization, predictive maintenance, and simulation Engage with customers, partners, and industry forums to validate solutions, influence standards, and position the portfolio in the market The must haves: Education: Bachelor's degree in Engineering or Software Engineering with telecommunications, networking, or communications systems concentration, or equivalent experience Experience: 15+ years of experience in the telecom software industry with at least 5+ years of product line management experience Application of telecom data modeling, schema design, graph or ontology structures, and enterprise data management tools Application of AI and ML data requirements including feature stores, training data pipelines, data lineage, and model grounding techniques for GenAI or LLM use cases Background in OSS environments including network automation, orchestration, inventory, assurance, and network management systems Exposure to telecom network domains including fixed, mobile or RAN, and cable or MSO across core, transport, and access layers Experience introducing complex technical products or architectures into Tier-1 CSP or network provider environments Nice to haves: Background in cloud-native architectures including Kubernetes, microservices, and cloud data platforms Exposure to multi-cloud environments Collaboration across engineering, product management, marketing, and field organizations Engagement with industry analysts and participation in analyst briefings Contribution to industry forums such as TM Forum, MEF, ETSI, or ONF Development of technical content including whitepapers, blogs, or conference presentations Support of go-to-market strategy and product positioning Ciena, we are committed to building and fostering an environment in which our employees feel respected, valued, and heard. Ciena values the diversity of its workforce and respects its employees as individuals. We do not tolerate any form of discrimination.Ciena is an Equal Opportunity Employer, including disability and protected veteran status.If contacted in relation to a job opportunity, please advise Ciena of any accommodation measures you may require.
04/06/2026
Full time
Blue Planet Data Management LeadPostulerlocations: London: UK- Reading-Regustime type: Full timeposted on: Publié aujourd'huijob requisition id: R031020As the global leader in high-speed connectivity, Ciena is committed to a people-first approach. Our teams enjoy a culture focused on prioritizing a flexible work environment that empowers individual growth, well-being, and belonging. We're a technology company that leads with our humanity-driving our business priorities alongside meaningful social, community, and societal impact.Ciena is advancing intelligent, automated networks through its Blue Planet portfolio by enabling data-driven innovation and AI-powered operations. This role leads the definition of data strategy, architecture, and productization for telecom data, enabling scalable digital twin capabilities and data platforms that support next-generation network operations. The position plays a critical role in aligning data foundations with AI, automation, and product innovation across the portfolio. How you will make an impact: Define and own the canonical telco data model and ontology across network topology, service lifecycle, inventory, assurance, and OSS domains Align data models with industry standards including TM Forum Open APIs, SID/eTOM, Open Digital Architecture, YANG/NETCONF, and TOSCA Drive adoption of ontology models across product teams as the semantic foundation for data exchange, AI training, and digital twin deployment Develop and lead the data fabric product architecture, including federated access, streaming pipelines, virtualization, metadata management, and lineage tracking Translate data architecture vision into scalable, cloud-native and microservices-based implementations in collaboration with engineering teams Define and deliver the telco digital twin strategy, including use cases such as network optimization, predictive maintenance, and simulation Engage with customers, partners, and industry forums to validate solutions, influence standards, and position the portfolio in the market The must haves: Education: Bachelor's degree in Engineering or Software Engineering with telecommunications, networking, or communications systems concentration, or equivalent experience Experience: 15+ years of experience in the telecom software industry with at least 5+ years of product line management experience Application of telecom data modeling, schema design, graph or ontology structures, and enterprise data management tools Application of AI and ML data requirements including feature stores, training data pipelines, data lineage, and model grounding techniques for GenAI or LLM use cases Background in OSS environments including network automation, orchestration, inventory, assurance, and network management systems Exposure to telecom network domains including fixed, mobile or RAN, and cable or MSO across core, transport, and access layers Experience introducing complex technical products or architectures into Tier-1 CSP or network provider environments Nice to haves: Background in cloud-native architectures including Kubernetes, microservices, and cloud data platforms Exposure to multi-cloud environments Collaboration across engineering, product management, marketing, and field organizations Engagement with industry analysts and participation in analyst briefings Contribution to industry forums such as TM Forum, MEF, ETSI, or ONF Development of technical content including whitepapers, blogs, or conference presentations Support of go-to-market strategy and product positioning Ciena, we are committed to building and fostering an environment in which our employees feel respected, valued, and heard. Ciena values the diversity of its workforce and respects its employees as individuals. We do not tolerate any form of discrimination.Ciena is an Equal Opportunity Employer, including disability and protected veteran status.If contacted in relation to a job opportunity, please advise Ciena of any accommodation measures you may require.
As the global leader in high-speed connectivity, Ciena is committed to a people-first approach. Our teams enjoy a culture focused on prioritizing a flexible work environment that empowers individual growth, well being, and belonging. We're a technology company that leads with our humanity-driving our business priorities alongside meaningful social, community, and societal impact. Ciena is advancing intelligent, automated networks through its Blue Planet portfolio by enabling data-driven innovation and AI-powered operations. This role leads the definition of data strategy, architecture, and productization for telecom data, enabling scalable digital twin capabilities and data platforms that support next generation network operations. The position plays a critical role in aligning data foundations with AI, automation, and product innovation across the portfolio. How you will make an impact Define and own the canonical telco data model and ontology across network topology, service lifecycle, inventory, assurance, and OSS domains Align data models with industry standards including TM Forum Open APIs, SID/eTOM, Open Digital Architecture, YANG/NETCONF, and TOSCA Drive adoption of ontology models across product teams as the semantic foundation for data exchange, AI training, and digital twin deployment Develop and lead the data fabric product architecture, including federated access, streaming pipelines, virtualization, metadata management, and lineage tracking Translate data architecture vision into scalable, cloud-native and microservices-based implementations in collaboration with engineering teams Define and deliver the telco digital twin strategy, including use cases such as network optimization, predictive maintenance, and simulation Engage with customers, partners, and industry forums to validate solutions, influence standards, and position the portfolio in the market Qualifications Education: Bachelor's degree in Engineering or Software Engineering with telecommunications, networking, or communications systems concentration, or equivalent experience Experience: 15+ years of experience in the telecom software industry with at least 5+ years of product line management experience Application of telecom data modeling, schema design, graph or ontology structures, and enterprise data management tools Application of AI and ML data requirements including feature stores, training data pipelines, data lineage, and model grounding techniques for GenAI or LLM use cases Background in OSS environments including network automation, orchestration, inventory, assurance, and network management systems Exposure to telecom network domains including fixed, mobile or RAN, and cable or MSO across core, transport, and access layers Experience introducing complex technical products or architectures into Tier 1 CSP or network provider environments Nice to haves Background in cloud-native architectures including Kubernetes, microservices, and cloud data platforms Exposure to multi-cloud environments Collaboration across engineering, product management, marketing, and field organizations Engagement with industry analysts and participation in analyst briefings Contribution to industry forums such as TM Forum, MEF, ETSI, or ONF Development of technical content including whitepapers, blogs, or conference presentations Support of go-to-market strategy and product positioning At Ciena, we are committed to building and fostering an environment in which our employees feel respected, valued, and heard. Ciena values the diversity of its workforce and respects its employees as individuals. We do not tolerate any form of discrimination. Ciena is an Equal Opportunity Employer, including disability and protected veteran status. If contacted in relation to a job opportunity, please advise Ciena of any accommodation measures you may require.
04/06/2026
Full time
As the global leader in high-speed connectivity, Ciena is committed to a people-first approach. Our teams enjoy a culture focused on prioritizing a flexible work environment that empowers individual growth, well being, and belonging. We're a technology company that leads with our humanity-driving our business priorities alongside meaningful social, community, and societal impact. Ciena is advancing intelligent, automated networks through its Blue Planet portfolio by enabling data-driven innovation and AI-powered operations. This role leads the definition of data strategy, architecture, and productization for telecom data, enabling scalable digital twin capabilities and data platforms that support next generation network operations. The position plays a critical role in aligning data foundations with AI, automation, and product innovation across the portfolio. How you will make an impact Define and own the canonical telco data model and ontology across network topology, service lifecycle, inventory, assurance, and OSS domains Align data models with industry standards including TM Forum Open APIs, SID/eTOM, Open Digital Architecture, YANG/NETCONF, and TOSCA Drive adoption of ontology models across product teams as the semantic foundation for data exchange, AI training, and digital twin deployment Develop and lead the data fabric product architecture, including federated access, streaming pipelines, virtualization, metadata management, and lineage tracking Translate data architecture vision into scalable, cloud-native and microservices-based implementations in collaboration with engineering teams Define and deliver the telco digital twin strategy, including use cases such as network optimization, predictive maintenance, and simulation Engage with customers, partners, and industry forums to validate solutions, influence standards, and position the portfolio in the market Qualifications Education: Bachelor's degree in Engineering or Software Engineering with telecommunications, networking, or communications systems concentration, or equivalent experience Experience: 15+ years of experience in the telecom software industry with at least 5+ years of product line management experience Application of telecom data modeling, schema design, graph or ontology structures, and enterprise data management tools Application of AI and ML data requirements including feature stores, training data pipelines, data lineage, and model grounding techniques for GenAI or LLM use cases Background in OSS environments including network automation, orchestration, inventory, assurance, and network management systems Exposure to telecom network domains including fixed, mobile or RAN, and cable or MSO across core, transport, and access layers Experience introducing complex technical products or architectures into Tier 1 CSP or network provider environments Nice to haves Background in cloud-native architectures including Kubernetes, microservices, and cloud data platforms Exposure to multi-cloud environments Collaboration across engineering, product management, marketing, and field organizations Engagement with industry analysts and participation in analyst briefings Contribution to industry forums such as TM Forum, MEF, ETSI, or ONF Development of technical content including whitepapers, blogs, or conference presentations Support of go-to-market strategy and product positioning At Ciena, we are committed to building and fostering an environment in which our employees feel respected, valued, and heard. Ciena values the diversity of its workforce and respects its employees as individuals. We do not tolerate any form of discrimination. Ciena is an Equal Opportunity Employer, including disability and protected veteran status. If contacted in relation to a job opportunity, please advise Ciena of any accommodation measures you may require.
Who Are We: Comply is the leading provider of compliance SaaS and consulting services for the global financial services sector. With more than 5,000 clients and hundreds of employees across the globe, Comply empowers Chief Compliance Officers and their teams to proactively manage regulatory obligations, mitigate risk, and scale with efficiency and confidence. Comply serves thousands of global financial services clients including broker-dealers, insurers, investment banks, private funds, RIAs, and wealth managers who rely on Comply offerings to power their compliance programs. To learn more about Comply, visit The Role: We are looking for Senior AI Data Engineers to implement and operationalize Comply's semantic layer - turning the ontological models defined by our ontologist and architects into working knowledge graphs, vector search infrastructure, and LLM-powered pipelines. This is a hands-on engineering role at the intersection of knowledge representation, AI infrastructure, and data platform engineering. You will own the delivery of semantic layer components, collaborate closely with application and data engineering teams, and ensure that AI-ready data products are reliable, performant, and adopted in practice. You will report into the Data and Analytics organization as part of a new team being created to enable future data capabilities in relation to our AI ambitions. Responsibilities: Semantic Layer Implementation Implement JSON-LD-based semantic models designed by the ontologist into production data systems Build and maintain knowledge graph structures that reflect canonical domain models • Develop and manage graph database schemas, queries, and data ingestion pipelines Ensure semantic consistency between ontology definitions and downstream data product AI & Vector Infrastructure Design and implement embedding pipelines that represent Comply's financial and regulatory data in vector space Build and operate vector database infrastructure for semantic search and similarity retrieval Implement RAG (Retrieval-Augmented Generation) architectures that ground LLM outputs in Comply's proprietary data Evaluate and integrate LLM tooling and frameworks appropriate to Comply's use cases Data Pipeline & Platform Engineering Build reliable, observable data pipelines that feed the semantic layer from upstream broker and regulatory data sources Apply DataOps practices including testing, monitoring, lineage tracking, and SLAs Work with Data Engineers and Backend Engineers to embed semantic models into APIs and data contracts Ensure the semantic layer scales with data volume and platform growth Collaboration & Enablement Partner closely with the Ontologist to ensure implemented models faithfully reflect domain intent Support consuming application teams in understanding and adopting AI-ready data products Contribute to resolving cross-domain data integration challenges Skills and Qualifications: Strong hands-on experience in data engineering, with a focus on semantic or AI data infrastructure Experience building and operating knowledge graphs or graph databases (e.g. Jena Fuseki, Neo4j, Amazon Neptune, or equivalent) Experience with vector databases and embedding pipelines (e.g. Pinecone, Weaviate, Qdrant, pgvector) Practical experience implementing RAG architectures or LLM-integrated data pipelines Familiarity with semantic web standards - JSON-LD, RDF, OWL, or SKOS Strong Python skills and experience with data pipeline frameworks Experience with cloud-native data platforms (AWS, Azure, or GCP) Exposure to domain-driven design (DDD) and bounded contexts is desirable. Experience working directly with ontologists or knowledge engineers is a plus. Familiarity with data contracts and data product frameworks is a plus. Experience with DataOps tooling, data reliability, or data observability platforms is desirable. Background in financial services, RegTech, or compliance data is a plus. To learn more about our values, mission and the wide-range of perks offered to employees at Comply, visit . Comply is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity, or national origin. Nothing in this job posting should be construed as an offer or guarantee of employment. Applicants must be authorized to work for any employer in the United Kingdom. Currently, we are unable to sponsor or take over sponsorship of an employment Visa at this time. Comply is aware of scammers posing as Comply employees and extending job offers via direct messaging, texts and social media platforms. These are fraudulent and should be treated as such. To learn more about this, please review our Statement of Fraudulent Job Offers.
30/05/2026
Full time
Who Are We: Comply is the leading provider of compliance SaaS and consulting services for the global financial services sector. With more than 5,000 clients and hundreds of employees across the globe, Comply empowers Chief Compliance Officers and their teams to proactively manage regulatory obligations, mitigate risk, and scale with efficiency and confidence. Comply serves thousands of global financial services clients including broker-dealers, insurers, investment banks, private funds, RIAs, and wealth managers who rely on Comply offerings to power their compliance programs. To learn more about Comply, visit The Role: We are looking for Senior AI Data Engineers to implement and operationalize Comply's semantic layer - turning the ontological models defined by our ontologist and architects into working knowledge graphs, vector search infrastructure, and LLM-powered pipelines. This is a hands-on engineering role at the intersection of knowledge representation, AI infrastructure, and data platform engineering. You will own the delivery of semantic layer components, collaborate closely with application and data engineering teams, and ensure that AI-ready data products are reliable, performant, and adopted in practice. You will report into the Data and Analytics organization as part of a new team being created to enable future data capabilities in relation to our AI ambitions. Responsibilities: Semantic Layer Implementation Implement JSON-LD-based semantic models designed by the ontologist into production data systems Build and maintain knowledge graph structures that reflect canonical domain models • Develop and manage graph database schemas, queries, and data ingestion pipelines Ensure semantic consistency between ontology definitions and downstream data product AI & Vector Infrastructure Design and implement embedding pipelines that represent Comply's financial and regulatory data in vector space Build and operate vector database infrastructure for semantic search and similarity retrieval Implement RAG (Retrieval-Augmented Generation) architectures that ground LLM outputs in Comply's proprietary data Evaluate and integrate LLM tooling and frameworks appropriate to Comply's use cases Data Pipeline & Platform Engineering Build reliable, observable data pipelines that feed the semantic layer from upstream broker and regulatory data sources Apply DataOps practices including testing, monitoring, lineage tracking, and SLAs Work with Data Engineers and Backend Engineers to embed semantic models into APIs and data contracts Ensure the semantic layer scales with data volume and platform growth Collaboration & Enablement Partner closely with the Ontologist to ensure implemented models faithfully reflect domain intent Support consuming application teams in understanding and adopting AI-ready data products Contribute to resolving cross-domain data integration challenges Skills and Qualifications: Strong hands-on experience in data engineering, with a focus on semantic or AI data infrastructure Experience building and operating knowledge graphs or graph databases (e.g. Jena Fuseki, Neo4j, Amazon Neptune, or equivalent) Experience with vector databases and embedding pipelines (e.g. Pinecone, Weaviate, Qdrant, pgvector) Practical experience implementing RAG architectures or LLM-integrated data pipelines Familiarity with semantic web standards - JSON-LD, RDF, OWL, or SKOS Strong Python skills and experience with data pipeline frameworks Experience with cloud-native data platforms (AWS, Azure, or GCP) Exposure to domain-driven design (DDD) and bounded contexts is desirable. Experience working directly with ontologists or knowledge engineers is a plus. Familiarity with data contracts and data product frameworks is a plus. Experience with DataOps tooling, data reliability, or data observability platforms is desirable. Background in financial services, RegTech, or compliance data is a plus. To learn more about our values, mission and the wide-range of perks offered to employees at Comply, visit . Comply is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity, or national origin. Nothing in this job posting should be construed as an offer or guarantee of employment. Applicants must be authorized to work for any employer in the United Kingdom. Currently, we are unable to sponsor or take over sponsorship of an employment Visa at this time. Comply is aware of scammers posing as Comply employees and extending job offers via direct messaging, texts and social media platforms. These are fraudulent and should be treated as such. To learn more about this, please review our Statement of Fraudulent Job Offers.
The Role: We are looking for Senior AI Data Engineers to implement and operationalize Comply's semantic layer - turning the ontological models defined by our ontologist and architects into working knowledge graphs, vector search infrastructure, and LLM powered pipelines. This is a hands on engineering role at the intersection of knowledge representation, AI infrastructure, and data platform engineering. You will own the delivery of semantic layer components, collaborate closely with application and data engineering teams, and ensure that AI ready data products are reliable, performant, and adopted in practice. You will report into the Data and Analytics organization as part of a new team being created to enable future data capabilities in relation to our AI ambitions. Responsibilities Semantic Layer Implementation Implement JSON LD based semantic models designed by the ontologist into production data systems Build and maintain knowledge graph structures that reflect canonical domain models and develop and manage graph database schemas, queries, and data ingestion pipelines Ensure semantic consistency between ontology definitions and downstream data products AI & Vector Infrastructure Design and implement embedding pipelines that represent Comply's financial and regulatory data in vector space Build and operate vector database infrastructure for semantic search and similarity retrieval Implement RAG (Retrieval Augmented Generation) architectures that ground LLM outputs in Comply's proprietary data Evaluate and integrate LLM tooling and frameworks appropriate to Comply's use cases Data Pipeline & Platform Engineering Build reliable, observable data pipelines that feed the semantic layer from upstream broker and regulatory data sources Apply DataOps practices including testing, monitoring, lineage tracking, and SLAs Work with Data Engineers and Backend Engineers to embed semantic models into APIs and data contracts Ensure the semantic layer scales with data volume and platform growth Collaboration & Enablement Partner closely with the Ontologist to ensure implemented models faithfully reflect domain intent Support consuming application teams in understanding and adopting AI ready data products Contribute to resolving cross domain data integration challenges Skills and Qualifications Strong hands on experience in data engineering, with a focus on semantic or AI data infrastructure Experience building and operating knowledge graphs or graph databases (e.g., Jena Fuseki, Neo4j, Amazon Neptune, or equivalent) Experience with vector databases and embedding pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector) Practical experience implementing RAG architectures or LLM integrated data pipelines Familiarity with semantic web standards - JSON LD, RDF, OWL, or SKOS Strong Python skills and experience with data pipeline frameworks Experience with cloud native data platforms (AWS, Azure, or GCP) Exposure to domain driven design (DDD) and bounded contexts is desirable. Experience working directly with ontologists or knowledge engineers is a plus. Familiarity with data contracts and data product frameworks is a plus. Experience with DataOps tooling, data reliability, or data observability platforms is desirable. Background in financial services, RegTech, or compliance data is a plus. Applicants must be authorized to work for any employer in the United Kingdom. Currently, we are unable to sponsor or take over sponsorship of an employment Visa at this time. Comply is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity, or national origin. Nothing in this job posting should be construed as an offer or guarantee of employment.
29/05/2026
Full time
The Role: We are looking for Senior AI Data Engineers to implement and operationalize Comply's semantic layer - turning the ontological models defined by our ontologist and architects into working knowledge graphs, vector search infrastructure, and LLM powered pipelines. This is a hands on engineering role at the intersection of knowledge representation, AI infrastructure, and data platform engineering. You will own the delivery of semantic layer components, collaborate closely with application and data engineering teams, and ensure that AI ready data products are reliable, performant, and adopted in practice. You will report into the Data and Analytics organization as part of a new team being created to enable future data capabilities in relation to our AI ambitions. Responsibilities Semantic Layer Implementation Implement JSON LD based semantic models designed by the ontologist into production data systems Build and maintain knowledge graph structures that reflect canonical domain models and develop and manage graph database schemas, queries, and data ingestion pipelines Ensure semantic consistency between ontology definitions and downstream data products AI & Vector Infrastructure Design and implement embedding pipelines that represent Comply's financial and regulatory data in vector space Build and operate vector database infrastructure for semantic search and similarity retrieval Implement RAG (Retrieval Augmented Generation) architectures that ground LLM outputs in Comply's proprietary data Evaluate and integrate LLM tooling and frameworks appropriate to Comply's use cases Data Pipeline & Platform Engineering Build reliable, observable data pipelines that feed the semantic layer from upstream broker and regulatory data sources Apply DataOps practices including testing, monitoring, lineage tracking, and SLAs Work with Data Engineers and Backend Engineers to embed semantic models into APIs and data contracts Ensure the semantic layer scales with data volume and platform growth Collaboration & Enablement Partner closely with the Ontologist to ensure implemented models faithfully reflect domain intent Support consuming application teams in understanding and adopting AI ready data products Contribute to resolving cross domain data integration challenges Skills and Qualifications Strong hands on experience in data engineering, with a focus on semantic or AI data infrastructure Experience building and operating knowledge graphs or graph databases (e.g., Jena Fuseki, Neo4j, Amazon Neptune, or equivalent) Experience with vector databases and embedding pipelines (e.g., Pinecone, Weaviate, Qdrant, pgvector) Practical experience implementing RAG architectures or LLM integrated data pipelines Familiarity with semantic web standards - JSON LD, RDF, OWL, or SKOS Strong Python skills and experience with data pipeline frameworks Experience with cloud native data platforms (AWS, Azure, or GCP) Exposure to domain driven design (DDD) and bounded contexts is desirable. Experience working directly with ontologists or knowledge engineers is a plus. Familiarity with data contracts and data product frameworks is a plus. Experience with DataOps tooling, data reliability, or data observability platforms is desirable. Background in financial services, RegTech, or compliance data is a plus. Applicants must be authorized to work for any employer in the United Kingdom. Currently, we are unable to sponsor or take over sponsorship of an employment Visa at this time. Comply is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, disability, sex, sexual orientation, gender identity, or national origin. Nothing in this job posting should be construed as an offer or guarantee of employment.
