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Our client is a leading global investment management company headquartered in London. It manages over $228 billion in assets and serves institutional investors, pension funds, wealth managers, and other sophisticated clients worldwide. The firm specializes in quantitative investing, alternative investments, systematic trading strategies, and technology-driven asset management. Data science, machine learning, and AI are core components of its investment and research processes.

As part of our collaboration we will focus on two foundational capabilities required to enable safe and scalable AI adoption across the enterprise: Agentic Security and AI-Ready Data Foundations.

Project overview:

• We build the data foundations and evaluation frameworks that make AI useful, reliable and safe inside regulated financial firms. The value of an AI agent depends not only on the models behind it, but also on the quality of the structured and unstructured data it consumes and the accuracy, relevance and traceability of the outputs it produces. Your job is to measure that quality, identify where it breaks down and turn the findings into practical improvements.
• This is an engineering role, not an analytical one. You will build the agentic workflows that reason over the firm's research content, and the ingestion, evaluation and guardrail tooling that makes their output trustworthy enough for investment professionals to act on. None of this tooling exists today — you would be building it from scratch.

Requirements:

• Proven experience building production agentic and LLM systems — multi-agent or orchestrated workflows that reason across heterogeneous sources (PDFs, audio transcripts, file shares, databases) and surface confidence, gaps and provenance back to end users.
• Hands-on experience engineering document ingestion and extraction pipelines : parsing, chunking and the automated quality controls around them — detecting empty or truncated content, vendor feeds delivering the wrong section of a document, duplication, encoding and OCR defects.
• Experience building evaluation and guardrail infrastructure for AI systems: groundedness scoring, citation and provenance (file name plus the exact snippet retrieved), eval harnesses, regression suites and LLM observability.
• Strong production Python engineering — services and pipelines that run unattended, with testing, CI and code standards. This is not a notebook-and-analysis role.
• Able to work from a deliberately vague brief, shape the problem directly with business stakeholders, and explain technical results to non-technical audiences.

Nice to have

• Experience tuning retrieval quality — chunking strategy, embedding choice, retrieval evaluation.
• Structured and time-series data-quality experience (coverage gaps, nulls in critical columns).
• ETL pipelines and fluency in SQL.
• Previous experience working with investment professionals in a fast-paced environment.
• Working knowledge of Snowflake, Linux/UNIX, Git, Jira.

Responsibilities:

• Build agentic workflows that reason over research reports, transcripts, filings and news, and present portfolio managers with a clear view of what was found, what is missing, and how confident the system is in each answer.
• Engineer automated quality checks on unstructured source content before ingestion — empty or blank content, truncation, extraction fidelity, coverage gaps across expected document sets.
• Build vendor delivery validation : detect and quantify parsing and format defects in incoming feeds, feed them back to vendors and the data sourcing team, and fix extraction where it sits with us.
• Build evaluation harnesses, benchmarks and guardrails for agent output — groundedness, factual accuracy, relevance, and citation/provenance, so any claim can be traced back to a specific file and snippet.
• Ship monitoring and dashboards surfacing data-quality findings, confidence levels and coverage gaps to both engineering and PM audiences.
• Work directly with the platform engineering team, data sourcing, and portfolio managers to turn business expectations into measurable, automated quality standards.

Why this position:

• This role sits at the intersection of data engineering, AI, and financial services, solving one of the most important challenges in enterprise AI: enabling agents to securely access and reason over trusted data. You'll have the opportunity to design and build foundational platforms that combine large-scale data systems, governance, and AI technologies in highly regulated environments. It offers significant technical ownership, exposure to cutting-edge AI agent architectures, and the chance to shape how organisations safely unlock value from their data.