About this role
Salary: £46,000 - 50,000 per year
Requirements: 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 back to end users.Hands-on experience engineering document ingestion and extraction pipelines, including parsing, chunking, and automated quality controls for empty or truncated content, incorrect document sections, duplication, encoding, and OCR defects.Experience building evaluation and guardrail infrastructure for AI systems, including groundedness scoring, citation and provenance, evaluation harnesses, regression suites, and LLM observability.Strong production Python engineering experience with services and pipelines that run unattended, including 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. 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, 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. Technologies: AIETLGitJIRALLMLinuxMachine LearningPythonSQLSecuritySnowflakeUnix More:
Our client is a leading global investment management firm headquartered in London, managing over $228B in assets. Technology, data science, machine learning, and AI are at the heart of our investment and research ecosystem. The project focuses on building two key capabilities for secure and scalable AI adoption: Agentic Security and AI-Ready Data Foundations. This is a hands-on engineering role and a greenfield initiative, giving us the opportunity to design and build the tooling from the ground up for use by investment professionals.
last updated 38 week of 2026