Hire RAG Developers in India — Retrieval Pipelines That Don't Hallucinate
Stop shipping AI features that make things up. CompanyBench matches you with pre-vetted RAG developers who build retrieval pipelines grounded in your own data — from embeddings and vector search to re-ranking and evaluation. Available on contract, contract-to-hire, or full-time.
Why a RAG Developer
An LLM on its own only knows what it was trained on — and it will confidently make things up when it doesn't know the answer. RAG (Retrieval-Augmented Generation) fixes that by grounding responses in your own documents, database, or knowledge base, so answers are accurate and traceable.
A RAG developer owns the full pipeline: document ingestion and chunking, embeddings, vector search, re-ranking, and evaluation — not just wiring up a vector database and calling it done. Need broader LLM coverage beyond retrieval? See Hire LLM Engineers.
RAG Hiring Tracks, One Talent Pool
Whether you need ingestion pipelines built, a vector search layer designed, or evaluation rigor added to an existing system — tell us your mix and we match accordingly.
Ingestion & Chunking Pipelines
Turning raw documents, databases, and knowledge bases into retrieval-ready chunks with the right splitting strategy.
Core Stack
Typical Use Case
Document Q&A systems and internal knowledge search grounded in real company data.
Embeddings & Vector Search
Choosing and implementing the right vector database and embedding model for your scale and infrastructure.
Core Stack
Typical Use Case
Semantic search, product discovery, and customer support assistants grounded in your own data.
Re-Ranking & Evaluation
Measuring retrieval quality against the RAG Triad instead of relying on subjective spot-checks.
Core Stack
Typical Use Case
Keeping a retrieval pipeline accurate and traceable as your document set and usage scale.
Skills & Tools We Cover
Our RAG developers work across the full retrieval stack, not just calling a vector database an integration.
Vector Databases
Embeddings & Retrieval
Frameworks
Evaluation
Deployment & Ops
How It Works
From requirements to a productive RAG developer, in days.
Share Requirements
Data sources, use case, and scale — tell us what you're building.
Get Matched
Pre-vetted RAG developers matched within 24-48 hours.
Interview & Select
Interview shortlisted RAG developers and select your fit.
Onboard & Start
Most hires are productive within a week.
Industries We've Staffed For
Fintech, Healthcare/MedTech, E-Commerce, and EdTech teams have hired RAG developers through CompanyBench for document Q&A, customer support assistants, and internal knowledge search grounded in real company data.
Fintech
Document Q&A and compliance search grounded in internal policy and regulatory data.
Healthcare/MedTech
Clinical knowledge search and patient-facing assistants grounded in verified source documents.
E-Commerce
Product search and customer support assistants grounded in catalog and policy data.
EdTech
Internal knowledge search and course-content Q&A grounded in real course material.
Related Hiring Resources
Compare adjacent talent pools before you hire.
Hire LLM Engineers
RAG is a core LLM engineering skill — the closest adjacent hiring track.
Hire Generative AI Developers
Broader GenAI application integration beyond the retrieval pipeline.
Hire AI Developers
Broader AI/ML, GenAI, and MLOps hiring tracks in one talent pool.
Hire Data Engineers
For buyers whose source data pipelines aren't ready for retrieval yet.
Frequently Asked Questions
Everything you need to know about hiring RAG developers through CompanyBench.
RAG (Retrieval-Augmented Generation) grounds an LLM's answers in your own data instead of relying purely on what the model was trained on — reducing hallucinations and making answers traceable to a source.
RAG is a specific technique within the broader LLM engineering discipline. Many LLM engineers work on RAG, but a dedicated RAG developer focuses specifically on the retrieval pipeline: ingestion, embeddings, vector search, and evaluation.
It depends on your scale and infrastructure. Our developers can advise on the right choice — Pinecone for simplicity, pgvector if you already run PostgreSQL, or Qdrant/Weaviate for richer self-hosted filtering.
Pre-vetted RAG developers are typically matched and available for interview within 24-48 hours.
Our developers evaluate systems against context relevance, answer faithfulness, and answer relevance — using frameworks like RAGAS and TruLens — rather than relying on subjective spot-checks.