Hire RAG Developers
- Senior AI + backend engineers
- Fixed price per project · no hourly billing
- Engineer assigned within 7 business days
Hire RAG developers who build retrieval-augmented generation that holds up on your real documents. We design chunking, embeddings, hybrid search and reranking, measure answer quality with evals, and ship it behind a FastAPI or Next.js app with tracing and cost limits.
Built for teams that need senior engineering — fast.
- US startups adding "chat with your docs" or AI search to their product
- Teams whose RAG prototype gives wrong or unsourced answers
- Companies with internal knowledge bases, PDFs or tickets they want searchable with AI
- CTOs who need a dedicated RAG engineer without hiring a full AI team
Concrete deliverables. No bench-warming.
Ingestion + chunking
PDF, HTML, Notion, Google Drive and database loaders, structure-aware chunking and metadata so retrieval finds the right passage.
Hybrid search + reranking
Vector search (pgvector, Pinecone, Qdrant, Weaviate) combined with keyword search and a reranker for higher answer accuracy.
Evals before launch
A test set built from your real questions, scored for retrieval quality and faithfulness, run on every change.
Citations + guardrails
Answers cite their sources, refuse when context is missing, and respect per-user document permissions.
Agentic RAG with LangGraph
Multi-step retrieval, query rewriting and tool use where a single search isn't enough — with loop limits and tracing.
Cost + latency control
Caching, model routing and context trimming so token bills and response times stay predictable.
Pick the shape that fits.
Dedicated RAG developer
A senior RAG engineer dedicated to your product for 3+ months, inside your team's workflow.
Project-based
Fixed scope, fixed price. A working RAG system on your data, with evals, delivered on a written timeline.
Retainer
Ongoing tuning, new data sources and model upgrades for an existing RAG system.
Audit + rescue
Diagnose why your RAG answers are wrong, slow or expensive, with a fix plan.
Tools our RAG engineers reach for.
Common questions about hiring RAG engineers.
What does a RAG developer do?+
A RAG developer builds the pipeline that lets an LLM answer from your own data: loading and chunking documents, embedding and indexing them, retrieving the right passages, and generating cited answers — then measuring quality with evals.
How much does it cost to hire RAG developers?+
Most initial RAG projects with us fall in the USD 3k–15k+ range depending on data sources and scope. Dedicated engineers are quoted as a fixed monthly fee — no hourly billing.
Which vector database do you use?+
Whatever fits your stack. pgvector is often enough if you already run Postgres; Pinecone, Qdrant or Weaviate when you need managed scale or advanced filtering.
Can you fix our existing RAG prototype?+
Yes. We start with an eval set from your real questions, find whether retrieval or generation is failing, and fix the weakest step first.
Can we interview the engineer first?+
Yes. We send 1–2 candidate engineers for a 30-minute interview each, and you pick the fit before anything is signed.
Ready to hire RAG engineers?
Send us a brief, we send 1–2 candidate engineers to interview within 7 business days. No long sales cycles.