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)
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 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

📌 Vector Db Engineer Karnataka
🏢 EXL
📍 Karnataka