Job Description
Role Purpose
nDeliver 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.
nKey Responsibilities
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- Vector index design & build — design and implement the vector indexing strategy over graph projections and entity attributes, including chunking, embedding selection and index configuration.n
- Embedding pipeline — build pipelines to generate, store and refresh embeddings as entity and graph data changes.n
- GraphRAG implementation — combine vector similarity search with graph structure and multi-hop traversal to produce grounded, context-rich retrieval.n
- Natural-language query enablement — implement and tune NLQ scenarios agreed with WK; support natural-language-to-graph-query translation approaches.n




- Retrieval evaluation & tuning — define and run evaluation harnesses measuring retrieval relevance and answer quality; tune retrieval parameters against agreed scenarios.n
- Grounding & traceability — ensure retrieved answers are attributable to source entities and edges, preserving provenance.n
- Performance & cost management — optimise index size, query latency and compute/token cost of retrieval operations.n
- Documentation — document retrieval architecture, evaluation results, known limitations and supported query patterns.n

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nMust-Have Qualifications
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- 5+ years engineering experience with 2+ years hands-on vector search / RAG implementationn
- Demonstrable production experience building a retrieval pipeline (not prototype-only)n
- Solid Python skills and familiarity with embedding modelsn
- Experience evaluating and tuning retrieval quality with defined metricsn
- Understanding of how to ground responses and preserve source traceabilityn

nNice-to-Have
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📌 Vector DB Engineer (Bengaluru Urban)
🏢 EXL
📍 Bengaluru Urban