RAG / AI Data Engineer

Many AI initiatives succeed or fail based on the quality, accessibility and governance of the underlying data. We work with organisations building the foundations required to support retrieval, search, agentic systems and production AI applications.

Role Overview Design and build data pipelines to support AI applications Develop Retrieval-Augmented Generation (RAG) architectures Create and maintain vector databases and knowledge repositories Structure, clean and prepare data for AI consumption Improve retrieval accuracy, relevance and performance Build scalable data foundations for AI products and agents Collaborate with architects, engineers and product teams to enable AI delivery Tools & Technologies (required) Python SQL Vector databases (Pinecone, Weaviate, Qdrant, Chroma or similar) Embedding models and retrieval frameworks LangChain, LlamaIndex or equivalent Data pipeline and ETL tooling Experience (required) Building data pipelines for AI or ML applications Designing or implementing RAG architectures Working with vector databases Managing structured and unstructured datasets Optimising retrieval quality and search performance Agent-based AI systems and workflows About You Strong understanding of data architecture and retrieval systems Able to balance accuracy, performance and scalability Comfortable working across structured and unstructured datasets Interested in practical AI implementation rather than theoretical research Focused on creating reliable foundations for AI systems Strong problem-solving and analytical skills

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