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Metro Bengaluru location and founders' brand increase competition, despite niche RAG specialization.
Highly specialized retrieval/RAG expertise limits transferability across unrelated industries.
Mandatory 7+ years and specific production RAG, embeddings, and vector-store expertise create strict hiring filters.
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Own end-to-end retrieval pipeline including document ingestion, chunking, embeddings, hybrid search, and reranking for research institutions and enterprise teams.
Build and manage dual-corpus retrieval integrating published literature and private institutional documents with provenance tracking.
Develop citation and identifier resolution systems and oversee retrieval evaluation with defined metrics and continuous improvements.
7+ years of experience in ML/backend engineering with production RAG, search, or information retrieval systems.
Strong proficiency in Python; hands-on experience with embeddings, vector stores, and rerankers; experience with graph databases is a plus.
Demonstrated ability in retrieval evaluation with metrics to measure and improve quality.
Role requires full-time onsite presence at HSR, Bengaluru (5 days a week).
Experienced in handling scientific or technical document corpora, preferably with familiarity in citation and identifier systems (e.g., DOI, arXiv).
Comfortable working in a small, fast-moving, low-process environment focusing on production engineering challenges like monitoring, reliability, cost, and latency.
Capable of operating with high judgment to design and freeze problem solutions and work closely alongside founders and AI agents to implement robust retrieval products.