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 (Bengaluru)
🏢 EXL
📍 Bengaluru