Vector DB Engineer

EXL; Bengaluru Urban; On-site; INR 3,500,000 - 6,500,000; Full time

Job summary

EXL is seeking a senior ML/Infra engineer to develop the natural-language query capability over an Entity Graph. You will implement the vector indexing, embedding pipelines, and GraphRAG retrieval to enable users to ask questions in plain language and receive grounded, source-backed answers.

The role emphasizes building production-grade retrieval pipelines, evaluating retrieval quality with defined metrics, and optimizing performance and cost.

Qualifications

• 5+ years of engineering experience with hands-on vector search/RAG work.
• Production-grade retrieval pipeline experience required.
• Strong Python and embedding-model familiarity.
• Experience evaluating retrieval quality with defined metrics.
• Ability to ground responses and preserve source traceability.

Responsibilities

• Design and build vector index over graph projections and attributes.
• Create and maintain embedding pipelines for dynamic data.
• Implement GraphRAG with grounded, context-rich retrieval.
• Enable natural-language queries and NL2GQL translation.
• Evaluate retrieval quality and tune parameters for accuracy.
• Ensure provenance and traceability of results.
• Optimize performance, latency, and cost of retrieval.
• Document architecture, results, and known limitations.

Skills

Vector search

RAG implementation

Python programming

Embedding models

Retrieval quality

Grounding provenance

Education

Bachelors in CS/Engineering

Tools

Graph databases

NL2GQL tooling

Python data stack

Job description

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)
• Strong 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
• 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