Partners Capital
We're looking for an AI Engineer who can take end to end ownership of features: from wrangling messy data, to building robust RAG pipelines, to shipping reliable APIs into production. You'll sit between "wrapper dev" and "researcher": comfortable with Python, data, and modern LLM tooling, and sceptical enough about AI outputs to keep our systems safe and accurate. Ideally, you have experience in agentic frameworks, some software development and strong desire to learn. Key responsibilities include: Design, build, and maintain Retrieval Augmented Generation (RAG) pipelines over unstructured data (PDFs, HTML, emails, transcripts, APIs) using embeddings, vector databases (e.g. Pinecone, Weaviate, Qdrant), and Graph Databases (PuppyGraph, Neo4J, TigerGraph, ArangoDB, etc). Implement and tune chunking strategies to preserve context and improve retrieval quality, rather than naïve fixed length splits. Integrate LLMs (OpenAI, Anthropic, open source) via SDKs/HTTP, handling context windows, rate limits, retries, timeouts, and graceful degradation. Experience with AI coding tools: Cursor, Windsurf, etc. Someone who can look to remove the 'vibe' from 'vibe coding', and call out AI when it produces poor code, based on their human coding experience. Leverage AI-assisted development (e.g., Cursor/Windsurf/GitHub Copilot/LLM agents) to create production quality software faster - generating scaffolding, tests, and documentation - while applying rigorous human review for correctness, security, and maintainability. Architecture & Agentic Systems Designing multi agent / "agentic" workflows where specialized AI agents coordinate (triage, research, drafting, review) to solve complex tasks. Experience with conversation state management, tool routing, and designing robust hand offs between agents/services. Security, governance & ethics Implementing prompt injection defences, output filtering, role/permissioning, and safe tool use patterns. Knowledge of data privacy and governance concerns around AI (GDPR, SOC2) and experience with dataset auditing / fairness evaluation is a plus. Experience fine tuning or adapting open source models (e.g. Llama, Mistral) and managing training/inference pipelines. Comfort experimenting with new architectures and tooling and evaluating trade offs vs. hosted APIs. Build ETL jobs and ingestion scripts to clean, normalize, and enrich text data (Python, pandas, BeautifulSoup or equivalent). Work with both SQL and vector stores; design schemas and indices that support low latency semantic and hybrid search. Design, iterate, and version prompts (system, user, tool) using techniques like few shot examples and chain of thought to improve reliability on complex tasks. Own evaluation (evals) for your features: create test sets, define success metrics (accuracy, faithfulness, latency), and run regression tests before and after changes (e.g. Ragas or custom eval harnesses). Monitor production behaviour, debug hallucinations, and systematically reduce failure modes through better retrieval, prompting, and guardrails (not just "tweak the temperature"). Backend integration & APIs Build and maintain typed, well tested backend services (e.g. Python with FastAPI/Flask; Node/TypeScript as a plus) that expose AI capabilities to front end and internal consumers. Implement observability for your services (logging, metrics, tracing, token usage) and contribute to dashboards for reliability and costs. Work closely with product, design, and ops to scope AI features, translate fuzzy requirements into concrete technical plans, and iterate based on user feedback. Take end to end ownership of projects: from prototype through to production, maintenance, and continuous improvement. Other Be responsible for ensuring all information security processes, policies and procedures are adhered to and any issues or concerns are raised with the Cyber Security team. Ensure full compliance with all local data protection regulations and privacy controls, and any related issues are raised via the appropriate channels. Key Requirements Technical Keeps up to date with the latest developments in AI and loves to experiment with new models. Bachelor's or Master's degree (preferred) in Computer Science, Artificial Intelligence, Data Science, or a related field - including formal AI/ML study (e.g., machine learning, deep learning, NLP, statistics) or equivalent professional training/portfolio. Nice to have: Experience with Microsoft Power Platform (Power Automate, Power Apps, Dataverse) and/or Azure Logic Apps for workflow automation and integrating AI services into business processes. Previous experience in investments/ finance sector or autonomous systems is a plus. Experience in: Natural Language Processing (NLP), Computer Vision, Reinforcement Learning, Generative AI (e.g., GPT, Stable Diffusion). Approximately 3+ years professional software engineering experience, including 1-2 years building production AI/ML or LLM based systems. (Strong non traditional backgrounds with equivalent portfolio are welcome.) Expert proficiency in Python for backend and data work (typing, async, packaging, testing, performance profiling); familiarity with TypeScript/Node/React is a strong plus. Hands on experience with at least one orchestration framework (e.g. LangChain, LlamaIndex, etc.). Practical experience implementing RAG: embeddings, vector databases (Pinecone, Weaviate, Milvus, Qdrant, etc.), and semantic search. Comfortable with SQL and working with pandas/DataFrame style data manipulation. Experience building and deploying APIs/microservices (REST, JSON, auth, rate limiting, pagination, error handling). Nice to have: Strong understanding of AI infrastructure for scalable model serving, distributed training, and GPU orchestration. Solid software engineering fundamentals: testing, code reviews, version control (Git), CI/CD, and basic cloud services (AWS/GCP/Azure). Nice to have: Experience with IAC tools like Terraform and Crossplane. Rapidly prototype AI solutions, test emerging tools, and recommend best practices for adoption. Knowledge of MCP (Model Context Protocol) is a plus. Ability to explain complex AI concepts simply to stakeholders. Working understanding of how LLMs behave in practice: context windows, tokens, temperature/top p, hallucinations, and prompt injection risks. Familiarity with core concepts: embeddings, vector similarity, Transformers at a conceptual level (you don't need to derive attention, but you should know what it does). Demonstrated experience shipping production AI feature (e.g. RAG chatbot, summarization/search assistant, agentic workflow, etc.). Portfolio or GitHub strongly preferred. Strong problem-solving skills with ability to translate business needs into AI solutions. Evolve with AI - you realise we are universal learners, always and forever improving and pushing the next frontier. 'Fail fast' - looking to have someone who experiments quickly and learns even quicker! "AI scepticism": you use AI tools for speed but verify outputs and design systems assuming models will sometimes be wrong. Product thinking: you care about UX quality, not just whether the API returns 200. Ability to work with ambiguity, learn new tools quickly, and keep up with a fast moving AI ecosystem. Partners Capital is committed to being a great place to work. We are focused both on wellbeing and professional growth. You can expect professional development and career progression opportunities, competitive compensation, exceptional benefits and a flexible "results-focused" working model. Our benefits package includes private medical and life insurance, income protection and pension contributions. In addition, we partner with organisations to provide wellness benefits. Partners Capital supports global philanthropy via a charity program and provides a volunteer day for all employees. We also champion a variety of social events.
We're looking for an AI Engineer who can take end to end ownership of features: from wrangling messy data, to building robust RAG pipelines, to shipping reliable APIs into production. You'll sit between "wrapper dev" and "researcher": comfortable with Python, data, and modern LLM tooling, and sceptical enough about AI outputs to keep our systems safe and accurate. Ideally, you have experience in agentic frameworks, some software development and strong desire to learn. Key responsibilities include: Design, build, and maintain Retrieval Augmented Generation (RAG) pipelines over unstructured data (PDFs, HTML, emails, transcripts, APIs) using embeddings, vector databases (e.g. Pinecone, Weaviate, Qdrant), and Graph Databases (PuppyGraph, Neo4J, TigerGraph, ArangoDB, etc). Implement and tune chunking strategies to preserve context and improve retrieval quality, rather than naïve fixed length splits. Integrate LLMs (OpenAI, Anthropic, open source) via SDKs/HTTP, handling context windows, rate limits, retries, timeouts, and graceful degradation. Experience with AI coding tools: Cursor, Windsurf, etc. Someone who can look to remove the 'vibe' from 'vibe coding', and call out AI when it produces poor code, based on their human coding experience. Leverage AI-assisted development (e.g., Cursor/Windsurf/GitHub Copilot/LLM agents) to create production quality software faster - generating scaffolding, tests, and documentation - while applying rigorous human review for correctness, security, and maintainability. Architecture & Agentic Systems Designing multi agent / "agentic" workflows where specialized AI agents coordinate (triage, research, drafting, review) to solve complex tasks. Experience with conversation state management, tool routing, and designing robust hand offs between agents/services. Security, governance & ethics Implementing prompt injection defences, output filtering, role/permissioning, and safe tool use patterns. Knowledge of data privacy and governance concerns around AI (GDPR, SOC2) and experience with dataset auditing / fairness evaluation is a plus. Experience fine tuning or adapting open source models (e.g. Llama, Mistral) and managing training/inference pipelines. Comfort experimenting with new architectures and tooling and evaluating trade offs vs. hosted APIs. Build ETL jobs and ingestion scripts to clean, normalize, and enrich text data (Python, pandas, BeautifulSoup or equivalent). Work with both SQL and vector stores; design schemas and indices that support low latency semantic and hybrid search. Design, iterate, and version prompts (system, user, tool) using techniques like few shot examples and chain of thought to improve reliability on complex tasks. Own evaluation (evals) for your features: create test sets, define success metrics (accuracy, faithfulness, latency), and run regression tests before and after changes (e.g. Ragas or custom eval harnesses). Monitor production behaviour, debug hallucinations, and systematically reduce failure modes through better retrieval, prompting, and guardrails (not just "tweak the temperature"). Backend integration & APIs Build and maintain typed, well tested backend services (e.g. Python with FastAPI/Flask; Node/TypeScript as a plus) that expose AI capabilities to front end and internal consumers. Implement observability for your services (logging, metrics, tracing, token usage) and contribute to dashboards for reliability and costs. Work closely with product, design, and ops to scope AI features, translate fuzzy requirements into concrete technical plans, and iterate based on user feedback. Take end to end ownership of projects: from prototype through to production, maintenance, and continuous improvement. Other Be responsible for ensuring all information security processes, policies and procedures are adhered to and any issues or concerns are raised with the Cyber Security team. Ensure full compliance with all local data protection regulations and privacy controls, and any related issues are raised via the appropriate channels. Key Requirements Technical Keeps up to date with the latest developments in AI and loves to experiment with new models. Bachelor's or Master's degree (preferred) in Computer Science, Artificial Intelligence, Data Science, or a related field - including formal AI/ML study (e.g., machine learning, deep learning, NLP, statistics) or equivalent professional training/portfolio. Nice to have: Experience with Microsoft Power Platform (Power Automate, Power Apps, Dataverse) and/or Azure Logic Apps for workflow automation and integrating AI services into business processes. Previous experience in investments/ finance sector or autonomous systems is a plus. Experience in: Natural Language Processing (NLP), Computer Vision, Reinforcement Learning, Generative AI (e.g., GPT, Stable Diffusion). Approximately 3+ years professional software engineering experience, including 1-2 years building production AI/ML or LLM based systems. (Strong non traditional backgrounds with equivalent portfolio are welcome.) Expert proficiency in Python for backend and data work (typing, async, packaging, testing, performance profiling); familiarity with TypeScript/Node/React is a strong plus. Hands on experience with at least one orchestration framework (e.g. LangChain, LlamaIndex, etc.). Practical experience implementing RAG: embeddings, vector databases (Pinecone, Weaviate, Milvus, Qdrant, etc.), and semantic search. Comfortable with SQL and working with pandas/DataFrame style data manipulation. Experience building and deploying APIs/microservices (REST, JSON, auth, rate limiting, pagination, error handling). Nice to have: Strong understanding of AI infrastructure for scalable model serving, distributed training, and GPU orchestration. Solid software engineering fundamentals: testing, code reviews, version control (Git), CI/CD, and basic cloud services (AWS/GCP/Azure). Nice to have: Experience with IAC tools like Terraform and Crossplane. Rapidly prototype AI solutions, test emerging tools, and recommend best practices for adoption. Knowledge of MCP (Model Context Protocol) is a plus. Ability to explain complex AI concepts simply to stakeholders. Working understanding of how LLMs behave in practice: context windows, tokens, temperature/top p, hallucinations, and prompt injection risks. Familiarity with core concepts: embeddings, vector similarity, Transformers at a conceptual level (you don't need to derive attention, but you should know what it does). Demonstrated experience shipping production AI feature (e.g. RAG chatbot, summarization/search assistant, agentic workflow, etc.). Portfolio or GitHub strongly preferred. Strong problem-solving skills with ability to translate business needs into AI solutions. Evolve with AI - you realise we are universal learners, always and forever improving and pushing the next frontier. 'Fail fast' - looking to have someone who experiments quickly and learns even quicker! "AI scepticism": you use AI tools for speed but verify outputs and design systems assuming models will sometimes be wrong. Product thinking: you care about UX quality, not just whether the API returns 200. Ability to work with ambiguity, learn new tools quickly, and keep up with a fast moving AI ecosystem. Partners Capital is committed to being a great place to work. We are focused both on wellbeing and professional growth. You can expect professional development and career progression opportunities, competitive compensation, exceptional benefits and a flexible "results-focused" working model. Our benefits package includes private medical and life insurance, income protection and pension contributions. In addition, we partner with organisations to provide wellness benefits. Partners Capital supports global philanthropy via a charity program and provides a volunteer day for all employees. We also champion a variety of social events.
Partners Capital
Finance Data & Analytics Manager, Principal London Position Description We are seeking a highly analytical and strategically minded Finance Data & Analytics Manager to join our Finance team. This role sits within Finance and is designed to bridge detailed data execution with forward-looking strategic thinking. The successful candidate will be responsible for consolidating and transforming multiple disparate data sources (Excel, SAP, Power BI and other internal systems) into robust, usable datasets that support financial consolidation, analysis and reporting. In addition, this role will support the effective maintenance, optimisation and utilisation of core finance systems, including SAP ERP and SAP Concur, ensuring they are configured and used in a way that maximises efficiency, data integrity and reporting capability. This is a hands-on role requiring comfort operating in the detail, while also contributing to the longer-term design and evolution of finance data architecture, systems usage and reporting capabilities. The role will also act as a key liaison between Finance and the Data / Transformation teams to ensure finance systems and data flows are aligned, scalable and fit for purpose. Key Responsibilities Data Integration & Consolidation Extract, reconcile and integrate data from multiple sources including SAP ERP, Excel models, Power BI, SAP Concur and other finance systems. Develop structured, reliable datasets for use in: Financial consolidation Management reporting Revenue analysis Forecasting and budgeting Improve data integrity, governance and consistency across finance outputs. Reduce manual processes and spreadsheet risk through improved data modelling and automation. Finance Systems Ownership & Optimisation Support the day-to-day maintenance and effective use of SAP ERP and SAP Concur within Finance. Ensure finance processes are optimally configured within SAP (e.g. chart of accounts, cost centres, reporting hierarchies). Drive continuous improvement in system usage, controls and reporting outputs. Partner with IT and Transformation teams on system upgrades, enhancements and integrations. Identify opportunities to automate workflows and improve efficiency across procure-to-pay, expense management and financial reporting processes. Ensure finance data structures within SAP support robust reporting, consolidation and analysis. Reporting & Analytics Build and maintain reporting models in Excel and Power BI. Develop dashboards and analytical tools to support: AUM tracking and analysis Revenue drivers (management fees, performance fees) Cost allocation and profitability analysis Business performance metrics Support senior finance leadership with high-quality analysis and insight. Move reporting from reactive to forward-looking and insight-driven. Strategic Finance Data Development Identify opportunities to enhance finance data architecture and reporting processes. Contribute to the development of a scalable, sustainable finance data environment. Translate finance reporting needs into structured data and system requirements. Support automation and finance transformation initiatives. Cross-Functional Collaboration Work closely with Data and Transformation teams to: Align finance data requirements with broader system architecture Improve integration between SAP, Concur and reporting tools Support system upgrades and finance transformation initiatives Act as a "translator" between Finance and technical teams. Ensure finance-specific nuances (e.g. revenue recognition, AUM flows, performance fee mechanics) are correctly reflected in data structures and system configurations. Other Be responsible for ensuring all information security processes, policies and procedures are adhered to and any issues or concerns are raised with the Cyber Security team. Ensure full compliance with all local data protection regulations and privacy controls, and any related issues are raised via the appropriate channels. Required Skills / Experience Experience Approximately 4-8+ years' experience in: Finance analytics FP&A Financial reporting Finance systems Or a finance-focused data / transformation role Experience within financial services preferred, ideally investment management. Strong understanding of: Assets under Management (AUM) Revenue drivers (management fees, performance fees) Cost structures within investment firms Experience working with ERP systems (SAP preferred). Experience with expense management systems (SAP Concur desirable). Qualifications Strong academic background preferred. Technical Skills Advanced Excel (complex modelling, Power Query, structured datasets). Strong Power BI capability (data modelling, DAX, dashboard design). Experience working with SAP ERP (essential). Familiarity with SAP Concur (desirable). Python (desirable but not essential). Strong understanding of data structures, data transformation and relational concepts. Partners Capital is committed to being a great place to work. We are focused both on wellbeing and professional growth. You can expect professional development and career progression opportunities, competitive compensation, exceptional benefits and a flexible "results-focused" working model. Our benefits package includes private medical and life insurance, income protection and pension contributions. In addition, we partner with organisations to provide wellness benefits. Partners Capital supports global philanthropy via a charity program and provides a volunteer day for all employees. We also champion a variety of social events.
Finance Data & Analytics Manager, Principal London Position Description We are seeking a highly analytical and strategically minded Finance Data & Analytics Manager to join our Finance team. This role sits within Finance and is designed to bridge detailed data execution with forward-looking strategic thinking. The successful candidate will be responsible for consolidating and transforming multiple disparate data sources (Excel, SAP, Power BI and other internal systems) into robust, usable datasets that support financial consolidation, analysis and reporting. In addition, this role will support the effective maintenance, optimisation and utilisation of core finance systems, including SAP ERP and SAP Concur, ensuring they are configured and used in a way that maximises efficiency, data integrity and reporting capability. This is a hands-on role requiring comfort operating in the detail, while also contributing to the longer-term design and evolution of finance data architecture, systems usage and reporting capabilities. The role will also act as a key liaison between Finance and the Data / Transformation teams to ensure finance systems and data flows are aligned, scalable and fit for purpose. Key Responsibilities Data Integration & Consolidation Extract, reconcile and integrate data from multiple sources including SAP ERP, Excel models, Power BI, SAP Concur and other finance systems. Develop structured, reliable datasets for use in: Financial consolidation Management reporting Revenue analysis Forecasting and budgeting Improve data integrity, governance and consistency across finance outputs. Reduce manual processes and spreadsheet risk through improved data modelling and automation. Finance Systems Ownership & Optimisation Support the day-to-day maintenance and effective use of SAP ERP and SAP Concur within Finance. Ensure finance processes are optimally configured within SAP (e.g. chart of accounts, cost centres, reporting hierarchies). Drive continuous improvement in system usage, controls and reporting outputs. Partner with IT and Transformation teams on system upgrades, enhancements and integrations. Identify opportunities to automate workflows and improve efficiency across procure-to-pay, expense management and financial reporting processes. Ensure finance data structures within SAP support robust reporting, consolidation and analysis. Reporting & Analytics Build and maintain reporting models in Excel and Power BI. Develop dashboards and analytical tools to support: AUM tracking and analysis Revenue drivers (management fees, performance fees) Cost allocation and profitability analysis Business performance metrics Support senior finance leadership with high-quality analysis and insight. Move reporting from reactive to forward-looking and insight-driven. Strategic Finance Data Development Identify opportunities to enhance finance data architecture and reporting processes. Contribute to the development of a scalable, sustainable finance data environment. Translate finance reporting needs into structured data and system requirements. Support automation and finance transformation initiatives. Cross-Functional Collaboration Work closely with Data and Transformation teams to: Align finance data requirements with broader system architecture Improve integration between SAP, Concur and reporting tools Support system upgrades and finance transformation initiatives Act as a "translator" between Finance and technical teams. Ensure finance-specific nuances (e.g. revenue recognition, AUM flows, performance fee mechanics) are correctly reflected in data structures and system configurations. Other Be responsible for ensuring all information security processes, policies and procedures are adhered to and any issues or concerns are raised with the Cyber Security team. Ensure full compliance with all local data protection regulations and privacy controls, and any related issues are raised via the appropriate channels. Required Skills / Experience Experience Approximately 4-8+ years' experience in: Finance analytics FP&A Financial reporting Finance systems Or a finance-focused data / transformation role Experience within financial services preferred, ideally investment management. Strong understanding of: Assets under Management (AUM) Revenue drivers (management fees, performance fees) Cost structures within investment firms Experience working with ERP systems (SAP preferred). Experience with expense management systems (SAP Concur desirable). Qualifications Strong academic background preferred. Technical Skills Advanced Excel (complex modelling, Power Query, structured datasets). Strong Power BI capability (data modelling, DAX, dashboard design). Experience working with SAP ERP (essential). Familiarity with SAP Concur (desirable). Python (desirable but not essential). Strong understanding of data structures, data transformation and relational concepts. Partners Capital is committed to being a great place to work. We are focused both on wellbeing and professional growth. You can expect professional development and career progression opportunities, competitive compensation, exceptional benefits and a flexible "results-focused" working model. Our benefits package includes private medical and life insurance, income protection and pension contributions. In addition, we partner with organisations to provide wellness benefits. Partners Capital supports global philanthropy via a charity program and provides a volunteer day for all employees. We also champion a variety of social events.