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) Robust 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 Fa
📌 Vector DB Engineer (Mumbai)
🏢 EXL
📍 Mumbai