> Markdown version of [/jobs/ext/4575007-data-engineer-document-retrieval-pipelines-contract](https://www.wearedevelopers.com/jobs/ext/4575007-data-engineer-document-retrieval-pipelines-contract). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - Document & Retrieval Pipelines - Contract - **Company:** VIQU Ltd - **Location:** London, UK - **Salary:** £130,000.0 - £169,000.0 - **Contract:** Permanent contract - **Skills:** HTML, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Cloud Computing, Continuous Integration, Github, Python (Programming Language), PostgreSQL, Metadata, OpenAI, Search Technologies, Google Cloud, Data Ingestion, Retrieval-Augmented Generation, Large Language Models, Indexer, Fastapi, SC Clearance, Build Management, Pytest, Pgvector, Kubernetes, Ollama, Data Pipelines, Docker - **Published:** November 5, 2026 - **Apply:** https://www.careerboard.com/gb/en/find-jobs-in-United-Kingdom/-89F7B77D427B613FDF/ ## About the Role * Clearance: Active SC clearance * Python: Strong Python, with experience building and testing data pipelines and APIs (FastAPI, PyTest) * Unstructured document ingestion: Extracting clean text from PDF, DOCX and HTML, including layout-aware parsing (headings, tables, multi-column pages) * RAG retrieval pipelines: Chunking strategy, embedding models, vector search (Postgres with pgvector or similar) and metadata design * Postgres: Schema design, indexing (including vector indexes) and performance at scale * Retrieval evaluation: Building labelled ground-truth datasets and measuring retrieval quality * Productionising code: Hardening pipelines (batching, idempotent re-ingestion, error handling), containerisation with Docker and CI/CD with GitHub Actions Desirable Experience * Cloud deployment, particularly Google Cloud; AWS (Bedrock, S3, ECR, EKS) is also used today * Kubernetes and Helm * Working with LLM providers (OpenAI, Anthropic, Bedrock) and locally hosted models (Ollama) * Data labelling workflows (eg Label Studio) ## Description Morela is supporting a specialist technology consultancy on a high-profile defence AI programme, and we are looking for a Data Engineer to own the document and retrieval pipelines behind a set of AI-enabled planning tools. This is a contract role on a two-year programme, Outside IR35, based in Central London. Active SC clearance is required. About the Client Our client is a specialist technology consultancy that embeds engineers directly into government and defence delivery teams. They are known for long-term programme work and for giving their engineers genuine ownership of what they build. This is one of their most strategically important programmes. The Role You will join a team building AI-enabled planning tools. The core of the role is document and retrieval pipelines: getting messy, real-world documents in, then making them structured, searchable and measurable. The first increment produced working prototypes. The next increment is about building on them to deliver reliable, production-grade services for real end users, so there will be a strong focus on scaling and hardening the pipelines. You will work as part of a mixed team of engineers, military and civil service colleagues, so being able to represent yourself well and explain technical decisions clearly matters as much as the engineering. Responsibilities * Build and maintain ingestion pipelines that extract clean text from PDF, DOCX and HTML, including layout-aware parsing of headings, tables and multi-column pages * Design and build retrieval pipelines for RAG, covering chunking strategy, embedding models, vector search and metadata design * Own the Postgres layer, including schema design, indexing (including vector indexes) and performance at scale * Build labelled ground-truth datasets and use them to measure and improve retrieval quality * Take prototype code to production, hardening pipelines with batching, idempotent re-ingestion and robust error handling * Containerise services and maintain CI/CD pipelines so changes ship reliably