Senior Python Engineer (Data Engineering & AI Agents)

Intellias; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Intellias is seeking a hands-on senior Python engineer with strong data engineering experience to advance AI agent capabilities and secure, scalable data foundations in an investment management context.

You will build catalogue, semantic, entitlement, and analytical layers enabling agents to access enterprise data safely and effectively. Expect collaboration with data governance and security disciplines to ensure data quality and trust.

Qualifications

• 6+ years building production Python software.
• Strong data engineering fundamentals and scalable pipelines.
• Experience with analytical/query engines (DuckDB/Trino/Spark/ClickHouse).
• Hands-on with LLM/agent applications: retrieval, vector stores, tooling.
• Knowledge of data governance: catalog, metadata, lineage, access control.
• Focus on data quality and trustworthy golden sources.

Responsibilities

• Build production-grade Python services and data pipelines over large data stores.
• Choose and implement appropriate query/analytical engines per workload.
• Develop catalogue, metadata, lineage and semantic layers for data discovery.
• Implement data access controls at the data source and for AI agents.
• Build agent-facing data access: retrieval, vector search, APIs with permissions.
• Apply LLMs to data work with humans in the loop and quality checks.
• Ensure golden sources, deduplication and data-quality checks at the source.
• Contribute to discovery and pragmatic, costed solution planning.

Skills

Python engineering

Data engineering

SQL

Parquet/columnar data

Data governance

LLM/agent apps

RAG / vector databases

RBAC/ABAC

Data quality

Tools

DuckDB

Trino

Spark

ClickHouse

Job description

Our client is a leading global investment management company headquartered in London, managing over $228 billion in assets. The firm is known for quantitative investing, systematic strategies, and technology-driven asset management, with data science, ML, and AI playing a key role in its research and investment processes.

Our work focuses on two key areas for secure, scalable AI adoption: Agentic Security and AI-Ready Data Foundations. The goal is to make large on-premise data estates accessible, understandable, traceable, and properly permissioned for AI agents.

This is a hands-on senior role for a strong Python engineer with solid data engineering experience and practical exposure to AI agents. You will build catalogue, semantic, entitlement, and analytical layers that enable agents to work with enterprise data safely and effectively.

Requirements:

• 6+ years building production software in Python, with strong engineering fundamentals (testing, performance, clean design).
• Solid data engineering: SQL, columnar formats (e.g. Parquet), pipeline design, and handling datasets large enough that naive approaches don’t scale.
• Hands-on experience with at least one analytical or query engine (e.g. DuckDB, Trino, Spark, ClickHouse).
• Real experience building LLM / agent applications: retrieval (RAG), vector databases, and tool / function calling.
• A working understanding of data governance: cataloguing, metadata, lineage, and access control (RBAC / ABAC).
• An instinct for data quality and trustworthy “golden” sources.

Will be a plus:

• Experience in on-premise / regulated environments and their constraints (data residency, auditability, “golden copy never moves”).
• Familiarity with semantic layers / knowledge graphs and entity resolution.
• Exposure to policy-as-code (e.g. OPA) or data-access platforms.
• Awareness of how AI agents are secured: identity, scoped access, evaluation and monitoring.
• Consulting or client-facing / pre-sales experience.

Responsibilities:

• Build production-grade Python services and data pipelines over large data stores (columnar / time-series and relational), and the queries that join across them.
• Select and implement the right query or analytical engine for each workload, rather than defaulting to one.
• Build catalogue, metadata, lineage and semantic layers that make data discoverable and consistently understood across teams.
• Implement access control that travels with the data: fusing sensitivity and licensing scope, enforced at the point of use, including for AI agents.
• Build agent-facing data access: retrieval (RAG), vector search, and APIs / MCP servers, with permissions applied before context reaches the model.
• Apply LLMs pragmatically to data work (metadata generation, classification, entity resolution) with humans in the loop and evaluate the quality of what the agents produce.
• Help keep data trustworthy: establish golden sources, deduplication and data-quality checks at the source.
• Contribute to discovery and solutioning: assessing current state, weighing build-vs-adopt, and shaping pragmatic, costed plans.