Location Bengaluru (hybrid)

Experience Indicative 5–8 years

Start Month 1–2

About The Role

Retrieval over a team's private research corpus — grounded, cited, and measurable — is the paid core of the

product. This role owns the capability end to end, from ingestion to retrieval quality, connecting across multiple

sources of information for research institutions and enterprise research teams.

Responsibilities
Own the retrieval pipeline: document ingestion, chunking, embeddings, hybrid search, reranking.
Build dual-corpus retrieval across published literature and private institutional documents, with provenance.
Build citation and identifier resolution (DOI/arXiv-class) and large-corpus onboarding.
Own retrieval evaluation: build the harness, set the metrics, prove improvement release over release.

Requirements
ML/backend engineering with production RAG, search, or IR systems experience.
Robust Python; hands-on with embeddings, vector stores, and rerankers; graph databases a plus.
Demonstrated evaluation rigor: you can show how you measured and improved retrieval quality.
Comfort with scientific or technical document corpora is a plus.

📌 Senior Ai Engineer Retrieval & Knowledge Rag Bengaluru
🏢 Fermi AI
📍 Bengaluru