Data Engineer - Document & Retrieval Pipelines - Contract

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

• 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)