Senior AI Engineer

Intellias; Slough; On-site; GBP 90,000 - 130,000; Full time

Job summary

Intellias partners with a leading global investment management firm to advance safe and scalable AI adoption across the enterprise. The role focuses on building production-grade agentic systems, robust data ingestion pipelines, and evaluation guardrails for transparent, citable outputs.

Candidate will work with platform engineering, data sourcing, and portfolio managers to turn vague business briefs into measurable quality standards and automated workflows that operate without supervision.

Qualifications

• Experience building production agentic and LLM systems.
• Engineering document ingestion and extraction pipelines with quality controls.
• Experience building evaluation guardrails, provenance, and groundedness scoring.

Responsibilities

• Build agentic workflows that reason over sources and present confidence and provenance to end users.
• Engineer automated quality checks on unstructured content before ingestion (empty content, truncation, extraction fidelity).
• Build vendor delivery validation to detect parsing defects and feed improvements back to sourcing teams.
• Build evaluation harnesses, guardrails, and benchmarks for agent output with traceable citations.
• Ship dashboards and monitoring showing data quality, confidence levels, and coverage gaps to engineers and PMs.
• Collaborate with platform engineering, data sourcing, and portfolio managers to translate business expectations into automated quality standards.

Skills

Agentic systems

Production Python

Data ingestion pipelines

Evaluation guardrails

Technical explanation

Tools

Snowflake

Linux/UNIX

Git

Jira

Job description

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.

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.