Senior AI Engineer

Salary: £46,000 - 50,000 per year

• Proven experience building production agentic and LLM systems, including multi-agent or orchestrated workflows that reason across heterogeneous sources and surface confidence, gaps, and provenance to end users.
• Hands-on experience engineering document ingestion and extraction pipelines, including parsing, chunking, and automated quality controls such as detecting empty or truncated content, wrong document sections, duplication, encoding issues, and OCR defects.
• Experience building evaluation and guardrail infrastructure for AI systems, including groundedness scoring, citation and provenance, eval harnesses, regression suites, and LLM observability.
• Strong production Python engineering experience, with services and pipelines that run unattended and include testing, CI, and code standards.
• Ability to work from a deliberately vague brief, shape the problem directly with business stakeholders, and explain technical results to non-technical audiences.
• Experience tuning retrieval quality, including chunking strategy, embedding choice, and retrieval evaluation.
• Structured and time-series data-quality experience, including coverage gaps and nulls in critical columns.
• ETL pipeline experience and fluency in SQL.
• Previous experience working with investment professionals in a fast-paced environment.
• Working knowledge of Snowflake, Linux/UNIX, Git, and Jira.

• 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, including empty or blank content, truncation, extraction fidelity, and coverage gaps across expected document sets.
• Build vendor delivery validation to 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, including 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.

• AI
• ETL
• Git
• JIRA
• LLM
• Linux
• Machine Learning
• Python
• SQL
• Security
• Snowflake
• Unix

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 are focused on two foundational capabilities required to enable safe and scalable AI adoption across the enterprise:
Agentic Security and AI-Ready Data Foundations.

last updated 36 week of 2026