Vector DB Engineer

Role Purpose

Deliver the natural-language query capability over the Entity Graph. This role implements the vector indexing and retrieval layer that powers GraphRAG — enabling users to ask questions in plain language instead of writing graph queries — and is accountable for the accuracy, relevance and evaluation of those responses.

Key Responsibilities

• Vector index design & build — design and implement the vector indexing strategy over graph projections and entity attributes, including chunking, embedding selection and index configuration.
• Embedding pipeline — build pipelines to generate, store and refresh embeddings as entity and graph data changes.
• GraphRAG implementation — combine vector similarity search with graph structure and multi-hop traversal to produce grounded, context-rich retrieval.
• Natural-language query enablement — implement and tune NLQ scenarios agreed with WK; support natural-language-to-graph-query translation approaches.
• Retrieval evaluation & tuning — define and run evaluation harnesses measuring retrieval relevance and answer quality; tune retrieval parameters against agreed scenarios.
• Grounding & traceability — ensure retrieved answers are attributable to source entities and edges, preserving provenance.
• Performance & cost management — optimise index size, query latency and compute/token cost of retrieval operations.
• Documentation — document retrieval architecture, evaluation results, known limitations and supported query patterns.

Must-Have Qualifications

• 5+ years engineering experience with 2+ years hands-on vector search / RAG implementation
• Demonstrable production experience building a retrieval pipeline (not prototype-only)
• Strong Python skills and familiarity with embedding models
• Experience evaluating and tuning retrieval quality with defined metrics
• Understanding of how to ground responses and preserve source traceability

Nice-to-Have

• Direct GraphRAG experience (graph + vector combined retrieval)
• Familiarity with Microsoft Fabric NL2GQL / Data Agent capabilities
• Exposure to graph databases and traversal concepts
• Experience managing LLM inference cost and latency at scale

Key Deliverables Owned

• Vector index over graph projections
• Embedding generation and refresh pipeline
• GraphRAG retrieval capability supporting agreed NLQ scenarios
• Retrieval evaluation results and tuning documentation
• Documented supported query patterns and known limitations