Founding AI Engineer - Proofline

Meraki Labs; Bengaluru; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Meraki Labs in Bengaluru (hybrid) is seeking a Senior AI Engineer — Retrieval & Knowledge (RAG) with 5–8 years of experience to own end-to-end retrieval capabilities in research and enterprise settings. You will manage ingestion, chunking, embeddings, and hybrid search, ensuring high-quality results across public and private corpora.

Responsibilities include building dual-corpus pipelines, provenance, DOI/arXiv-style identifiers, and evaluation harnesses to measure and improve retrieval quality

Qualifications

• 5–8 years of experience in ML/backend engineering with production RAG or IR systems.
• Strong Python skills with embeddings and vector-store tooling.
• Experience evaluating retrieval quality and system performance.

Responsibilities

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

Skills

Python

Embeddings

Vector stores

IR systems

RAG

Tools

Graph databases

Job description

Senior AI Engineer — Retrieval & Knowledge (RAG)
Location Bengaluru (hybrid)
Experience Indicative 5–8 years

About The Role

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.
• Strong 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.