Founding AI Engineer - Proofline

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.
• Strong Python; hands-on with embeddings, vector stores, and rerankers; graph databases a plus.
• Demonstrated evaluation rigour: you can show how you measured and improved retrieval quality.
• Comfort with scientific or technical document corpora is a plus.
  How We Work

Small team, high trust, written decisions. Designs get adversarial review before code; PRs get automated review driven to zero open findings; features aren't done until verified on a live system. AI agents do a large share of the implementation—your leverage is judgment: framing the problem, freezing the right design, and knowing when the machine is wrong.

You Should Apply If

• You enjoy turning messy problems into concrete solutions.
• You thrive in fast-moving, low-process environments.
• You're excited about production engineering (monitoring, reliability, cost, latency).

You Should Not Apply If

• You want remote/hybrid (this is onsite Bangalore only).
• You prefer narrow tickets and minimal ambiguity.
• You prefer highly structured, slow-moving product organizations.

The Recruiter for this role is Ritesh Kalvellu. Apply to the role directly, and he will get back to you if your profile is relevant to the hiring requirements.