Job DescriptionRole 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) Solid 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 Micros
📌 Vector DB Engineer (Bengaluru)
🏢 EXL
📍 Bengaluru
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