Senior AI/RAG Engineer (Document Intelligence)

Location: Remote from Spain (an indefinite Spanish employment contract)

Our client is a leading general 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 that make AI useful and safe inside regulated financial firms. The value of AI is capped by the data its agents can reach: if an agent cannot find, interpret, trace or be correctly permissioned against data, the capability is useless, or worse, unsafe. Your job is to close that gap.

This is a hands-on senior role for an excellent Python engineer with strong data-engineering skills who is genuinely comfortable building with AI agents. You will design and build the catalogue, semantic, entitlement and analytical layers that turn large on-premise data estates into something agents can use.

Requirements:

- 5+ years of production Python development, including 2+ years of LLM and RAG engineering in production: retrieval pipelines, vector stores, structured extraction, and the surrounding operational tooling.
- Strong experience building evaluation harnesses: versioned test suites derived from real question banks, separate scoring of retrieval and answers, scoring where a correct "not found" counts as a pass, and suites wired into delivery as release gates (e.G. Langfuse, RAGAS, DeepEval or similar, plus custom metrics).
- Hybrid retrieval engineering: keyword and semantic search combined, result merging, cross-encoder reranking,



tuning against measured baselines, working within a platform-fixed embedding model and index.
- Structure-aware document processing: layout-aware parsing and chunking that keeps tables intact (e.G. Docling, Tika or similar), including OCR handling for scanned documents and multilingual content.
- LLM extraction at scale: schema-driven extraction of attributes, entities, clauses and relationships with per-field confidence, calibrated thresholds and a human review loop (e.G. Label Studio or similar), piloted and measured before scale-out.
- Strong PostgreSQL: typed relational modelling plus JSONB, schema-as-code with migration tooling, derived views managed as tested transformations.
- Provenance and citation discipline: every extracted fact traceable to its source document and passage;
answers that state explicitly when something could not be confirmed.
- Fluent English for written and spoken communication with client teams.

Will be a plus

- Integration with SharePoint and Microsoft Graph APIs or an equivalent enterprise content platform: change notifications, delta polling, permission metadata.
- Bitemporal modelling (execution versus effective dates, as-of queries) and document lineage or supersession modelling.
- Graph engines (e.G. Apache AGE, Neo4j or similar);
controlled knowledge graphs with provenance on every element.
- Legal, contract or fund-documentation domain knowledge, or demonstrated ability to learn a document domain in depth.
- Permission-aware retrieval: access lists stored in the index, group resolution at query time, denial on uncertainty (the entitlement design is owned by a parallel workstream;
this role implements against it).
- Token-efficiency engineering: corpus preparation, section slicing, cost measurement per query.
- Exposing capabilities to an assistant platform as MCP tools or skills;
day-to-day use of AI coding agents.
- Experience in financial services or other regulated on-premise environments;
client-facing experience.

Responsibilities:

- Build the evaluation foundation at the start: convert the client's existing question

📌 Senior Ai/Rag Engineer (Document Intelligence) (Castro Urdiales)
🏢 Intellias
📍 Castro Urdiales