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Project description We are seeking a Senior Semantic Engineer to implement semantic data frameworks that provide a shared structure for enterprise data. In this role you will focus on building and maintaining ontologies and knowledge graphs, enforcing semantic validation rules for data quality, and collaborating with AI teams to integrate these semantic structures into intelligent applications. The position is industry-agnostic, emphasizing strong semantic web expertise and the ability to apply it in any enterprise context. Responsibilities Ontology Design & Maintenance: Design, develop, and maintain ontologies (using OWL/RDF or similar) that model key enterprise data domains and relationships, ensuring a consistent and shared data vocabulary across the organization. This includes collaborating with domain experts to capture real-world concepts and validate that the ontology accurately represents business knowledge. Knowledge Graph Development: Build and manage enterprise knowledge graphs based on the defined ontologies, linking diverse data sources into a unified graph data model. This involves configuring graph databases or triple stores, populating the knowledge graph with data (RDF triples), and optimizing it for query performance and scalability. Semantic Querying (SPARQL): Create and optimize SPARQL queries to enable efficient retrieval, integration, and analysis of data from the knowledge graph. You will develop semantic queries and endpoints that support advanced search and analytics use cases, making it easier for others to retrieve insights from linked data. Validation Rules & Data Quality: Implement semantic validation rules and consistency checks (e.g., using SHACL or OWL constraints) to ensure data integrity and quality within the ontology and knowledge graph. You will define and enforce data modelling conventions and business rules so that enterprise data conforms to the ontology's standards and remains interoperable across systems. Integration with Enterprise Systems: Work closely with software engineers, data architects, and IT teams to integrate the ontology and knowledge graph into the organization's existing data infrastructure and workflows. This includes embedding semantic models in data pipelines, APIs, and databases, so that enterprise applications can produce and consume linked data seamlessly. Collaboration & Cross-Functional Support: Collaborate with cross functional teams and stakeholders. For example, partner with AI/ML teams to incorporate the knowledge graph into AI driven solutions, and team up with business analysts or data stewards to align the semantic models with business needs. You will communicate semantic concepts to non technical stakeholders, providing training or documentation to ensure adoption of the semantic framework across the organisation. Integration with AI Agents: Work with AI agents and large language model (LLM) teams to leverage the ontology and knowledge graph for intelligent applications. For instance, you might enable an AI chatbot to use the knowledge graph for more context aware responses, or develop mechanisms for AI systems to perform reasoning over the ontologies. This responsibility ensures that semantic data structures enhance AI initiatives (e.g. improving context, disambiguation, and knowledge retrieval in AI workflows). Standards & Best Practices: Stay current with emerging semantic web standards, tools, and best practices. Continuously improve the semantic architecture by adopting relevant metadata standards and ensuring alignment with industry best practices for ontologies and knowledge graphs. You will also contribute to establishing internal guidelines and best practices for semantic data management, promoting a culture of well structured, semantically rich data across the enterprise. SKILLS Must have Ontology Design & Maintenance: Design, develop, and maintain ontologies (using OWL/RDF or similar). Semantic Web Proficiency: Strong knowledge of semantic web technologies and standards - specifically, hands on proficiency with OWL (Web Ontology Language) and RDF (Resource Description Framework) for ontology modelling, as well as SPARQL for querying graph data. Knowledge Graph Experience: Practical experience building or maintaining knowledge graphs or linked data systems in an enterprise setting. Data Modelling & Integration Skills: A solid understanding of data modelling principles, data architecture, and integrating heterogeneous data sources. You should be capable of abstracting real world entities into a semantic schema and mapping relational or NoSQL data to an ontology. Programming Skills: Proficiency in at least one programming or scripting language (such as Python, Java, or similar) Nice to have Metadata Standards: Familiarity with metadata standards and vocabularies such as Dublin Core, schema.org, or other industry specific ontologies/taxonomies. Experience applying these standards to annotate or integrate data AI and LLM Integration: Experience working on projects that involve AI agents or large language models, where ontologies or knowledge graphs were used to improve AI performance Enterprise System Integration: Proven experience integrating semantic technologies into existing enterprise systems or data platforms Tools & Platforms: Hands on experience with ontology and knowledge graph tools is beneficial

About Northern Trust As a global leader in innovative wealth management, asset servicing, asset management and banking services, Northern Trust (Nasdaq: NTRS) is proud to guide the world's most successful individuals, families, corporations and institutions. Since 1889, we have aligned our efforts with our three guiding Principles That Endure: Service, Expertise, and Integrity. Together, they reflect the three cornerstones of business conduct which we strive to instill in our employees, whom we call partners, and to provide to our clients and the communities we serve worldwide. With more than 135 years of financial experience and over 24,000 partners, we serve the world's most sophisticated clients using leading technology and exceptional service. The Senior Information Architect sits within Northern Trust's Information Architecture team, shaping the firm's enterprise data strategy, data modelling standards, and AI-enabled capabilities. The role will provide design authority and governance across conceptual, logical, and physical data models, operating across the firm's cloud data platforms and contributing to AI-driven data products. Data Modelling & Architecture Lead and contribute with conceptual, logical, and physical data model design aligned to the firm's data mesh and medallion architectures Govern and maintain the enterprise data model library in SqlDBM, enforcing naming conventions, domain standards, and versioning Define and steward canonical data entities and lineage across source, integration, and consumption layers Help with the translation of business requirements into rigorous data structures in collaboration with engineering and domain teams Guide teams on the design and effective use of semantic data models, including their application to agentic AI solutions Semantic & Knowledge Modelling Integrate semantic layers with business glossaries, data catalogues, and lineage tooling Design and maintain semantic models to support agentic-AI capabilities Help develop ontologies, taxonomies, and controlled vocabularies underpinning enterprise metadata and search Define semantic modelling standards and patterns to ensure consistent meaning, interoperability, and AI readiness across data products Guide and control the creation of data domains that align with the banks underlying capabilities Data Engineering & Platforms Design data product schemas across Snowflake and Databricks, including Delta Lake structures and access patterns Define and enforce architectural guardrails for data product design, including performance, scalability, security, and cost efficiency Set design standards and review transformation/pipelines for compliance Embed data contract standards and data quality frameworks across pipeline outputs Agentic AI & AI-Assisted Development Leverage Snowflake Cortex / Databricks Genie (or similar) to help engineering teams deliver natural language query and LLM-augmented data products Utilize GitHub Copilot (or similar) to accelerate modelling, documentation, and pipeline development Design and define a set of agentic skills that can be provided to implementation teams Contribute to the firm's AI Champion network, evaluating emerging capabilities and advising on adoption Enterprise Architecture & Design Assurance Act as a design authority for data architecture across teams and data domains Partner with enterprise, security, and risk architecture to ensure data solutions align with broader technology and regulatory standards Provide early-stage architectural input to initiative discovery and solution design to reduce delivery risk Governance & Stakeholder Engagement Present architecture decisions to senior stakeholders, architecture review boards, and regulatory audiences Support regulatory compliance through robust lineage, metadata management, and data quality evidence Mentor junior architects and engineers within the Information Architecture community of practice Ideal Candidate Proven experience in data architecture, data engineering, or information management Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related discipline Proven enterprise data modelling experience at conceptual, logical, and physical levels Hands on SqlDBM experience (or equivalent modern toolset) Proven experience with dbt Labs Good Python skills, API integration, automation Practical Snowflake and Databricks experience including Cortex AI and Genie Working knowledge of OWL, RDF, SPARQL and enterprise ontology design Experience with at least one agentic AI framework GitHub Copilot or equivalent AI coding assistant experience in a professional setting Financial services background preferred; BCBS 239 awareness advantageous Desirable Skills Data Vault 2.1 alongside Kimball/Star-schema modelling Data mesh, data product ownership and federated governance Knowledge graph platforms (Neo4j, Neptune) or knowledge of OWL, RDF and exposure to and build of ontologies Published thought leadership or community contributions in data or AI Work Authorization Applicants must have the right to work in the United Kingdom at the time of application and for the duration of employment. Please note that Northern Trust is unable to provide visa sponsorship for this role. This includes Skilled Worker visas, Global Business Mobility routes, Graduate visas, Youth Mobility Scheme, High Potential Individual visas, Scale-up Worker visas, Temporary Worker visas, and other employer-sponsored visa categories. Working with Us As a Northern Trust partner, you will be part of a flexible and collaborative work culture, which has a strong history of financial strength and stability. Movement within the organization is encouraged, senior leaders are accessible, and you can take pride in working for a company committed to an inclusive workplace and assisting the communities we serve. Philanthropy is deeply rooted in Northern Trust's history and is an essential element of our culture. Employees around the world give their time and talent to work for the greater good of their communities. Reasonable Accommodation Northern Trust is committed to working with and providing adjustments to individuals with health conditions and disabilities. If you need a reasonable accommodation for any part of the employment process, please email our HR Service Center at , or alternatively you can discuss your individual requirements with the recruiter you are working with.

Overview Make your mark for patients. We are looking for an agile and innovative Data Foundations Lead to join the Patient Solutions Digital Strategy & Innovation team, based in Slough, UK (Windlesham, Surrey from 2027), Braine l'Alleud, Belgium or Boston, USA. About the role: In this role, you will establish the data foundations that enable Patient Solutions to scale trusted, reusable and AI-ready data across Research and Early Development. You will define and govern metadata, ontologies, identifiers and data standards that improve data quality, interoperability and discoverability. Working across scientific, digital and enterprise teams, you will help shape how data is created, governed and consumed to accelerate research, innovation and AI-driven decision making. What you'll do Establish and govern enterprise ontologies, business vocabularies and global identifiers to ensure consistent representation and traceability of data across Patient Solutions. Design and implement scalable metadata management capabilities that improve data quality, lineage, interoperability and discoverability across structured and unstructured data. Drive the adoption of AI-enabled approaches to metadata generation, curation and knowledge management. Partner with data owners and scientific teams to embed data standards and governance at the point of data creation. Define scalable conceptual and logical data models that support research, scientific and business use cases. Design and deliver reusable domain data products supporting strategic programmes and AI initiatives. Promote FAIR data principles and modern data foundation practices across Patient Solutions. Collaborate with Enterprise Architecture to ensure alignment between business capabilities, data standards and technology platforms. Influence stakeholders across scientific and digital communities to drive adoption of data governance and foundation capabilities. Education, experience and skills Education: Bachelor's or Master's degree in Computer Science, Data Science, Information Management, Bioinformatics or a related discipline. Experience: +8 years of experience in Data Architecture, Data Foundations, Data Governance, Research Informatics, or a closely related field. Experience establishing enterprise data foundations, including metadata management, ontology management and data governance. Experience designing conceptual and logical data models in complex enterprise environments. Experience working with research, scientific or life sciences data is highly desirable. Experience implementing AI-enabled approaches for metadata management, knowledge management or information discovery. Experience defining data products, data standards and information models. Experience collaborating with multidisciplinary stakeholders, including scientists, business teams and technology teams. Skills Strong understanding of metadata management, data lineage and data quality principles. Knowledge of ontology management, semantic technologies or knowledge graphs. Understanding of structured and unstructured data management. Excellent stakeholder engagement, facilitation and communication skills. Ability to influence without direct authority in complex matrix organisations. DAMA, CDMP, TOGAF or similar certifications are considered an advantage. Are you ready to 'go beyond' to create value and make your mark for patients? If this sounds like you, then we would love to hear from you! About us UCB is a global biopharmaceutical company, focusing on neurology and immunology. We are over 9,000 people in all four corners of the globe, inspired by patients and driven by science. Why work with us? At UCB, we don't just complete tasks, we create value. We aren't afraid to push forward, collaborate, and innovate to make our mark for patients. We have a caring, supportive culture where everyone feels included, respected, and has equal opportunities to do their best work. We 'go beyond' to create value for our patients, and always with a human focus, whether that's on our patients, our employees, or our planet. Working for us, you will discover a place where you can grow, and have the freedom to carve your own career path to achieve your full potential. At UCB, we've embraced a hybrid-first approach to work, bringing teams together in local hubs to foster collaborative curiosity. Unless expressly stated in the description or precluded by the nature of the position, roles are hybrid with 40% of your time spent in the office. UCB is an equal opportunity employer. All employment decisions will be made without regard to any characteristic protected by applicable laws. Should you require any adjustments to our process to assist you in demonstrating your strengths and capabilities contact us on . Please note should your enquiry not relate to adjustments; we will not be able to support you through this channel.

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