RAG engineers, vetted and backed
Hire RAG engineers who ship grounded answers, not confident guesses
A RAG (retrieval-augmented generation) engineer builds the retrieval layer that feeds an LLM the right context from your own documents, so answers are grounded and cite their sources instead of hallucinating. They own chunking, embeddings, vector search, reranking, and evaluation. Grape5 places pre-vetted, dedicated RAG engineers, managed and backed, with a free replacement if the fit is wrong.
In short
A RAG (retrieval-augmented generation) engineer builds the retrieval layer that feeds an LLM the right context from your own documents, so answers are grounded and cite their sources instead of hallucinating.
They own chunking, embeddings, vector search, reranking, and evaluation. Grape5 places pre-vetted, dedicated RAG engineers, managed and backed, with a free replacement if the fit is wrong.
When to hire RAG engineers
- You want an internal support assistant that answers from your help center and policy docs and cites the exact article, so your team and customers stop getting made-up answers.
- You have an LLM feature that guesses instead of using your proprietary data (product manuals, contracts, past tickets), and you need it grounded in your own content.
- Your RAG demo impressed everyone but returns wrong or irrelevant answers at production scale, and you need someone to fix retrieval, add reranking, and build a real eval harness.
- You are searching regulated documents (legal, clinical, financial) where every answer must carry a source citation and an audit trail, and must refuse when the context is thin.
How we vet RAG engineers
Every engineer we put forward is screened by a senior Grape5 engineer before you meet them. For RAG engineers, we look specifically at:
- Chunking and embeddings: whether they can defend chunk size and overlap, choose semantic versus fixed splitting, pick an embedding model for the domain, and explain when re-embedding is needed.
- Retrieval quality: hybrid search (dense plus BM25), reranking with cross-encoders, metadata filtering, and diagnosing whether a bad answer came from low recall or low precision.
- Evaluation: building a golden question set and measuring faithfulness, context precision, and answer relevancy (for example with RAGAS), plus catching hallucination and the lost-in-the-middle problem.
- Vector store operations: pgvector, Pinecone, Qdrant, or Weaviate, HNSW versus IVF tradeoffs, index freshness, and handling document updates and deletes without leaving stale results.
- Grounding and safety: prompt construction that forces citations and a refusal when context is insufficient, and guarding against prompt injection that arrives inside retrieved content.
Grape5 vs a freelancer marketplace
Grape5
- Who the engineer works for
- Vetted, dedicated, and backed by Grape5 for your engagement.
- Vetting
- Screened by our own senior engineers, code, system design and communication, before you ever meet them.
- Timezone
- 4+ hours of daily overlap with your US working hours, in your tools and standups.
- If it isn't working
- We replace them from the bench, usually within days, at no extra cost.
- Continuity
- The same team, retained and growing with your product.
A freelancer marketplace
- Who the engineer works for
- An independent contractor juggling several clients at once.
- Vetting
- Self-reported skills, a résumé and a star rating.
- Timezone
- Whatever hours the contractor decides to keep.
- If it isn't working
- You re-post the role and start the search from scratch.
- Continuity
- Churn between contracts, the context leaves when they do.
| Grape5 | A freelancer marketplace | |
|---|---|---|
| Who the engineer works for | Vetted, dedicated, and backed by Grape5 for your engagement. | An independent contractor juggling several clients at once. |
| Vetting | Screened by our own senior engineers, code, system design and communication, before you ever meet them. | Self-reported skills, a résumé and a star rating. |
| Timezone | 4+ hours of daily overlap with your US working hours, in your tools and standups. | Whatever hours the contractor decides to keep. |
| If it isn't working | We replace them from the bench, usually within days, at no extra cost. | You re-post the role and start the search from scratch. |
| Continuity | The same team, retained and growing with your product. | Churn between contracts, the context leaves when they do. |
Related roles you can hire
Pre-vetted engineers across adjacent skills, dedicated to your product and your US working hours.
Frequently asked questions
A general engineer can call an LLM API. A RAG engineer owns the retrieval layer that decides which of your documents reach the model, and that is where most RAG projects succeed or fail. The real craft is chunking, embeddings, hybrid retrieval, reranking, and honest evaluation, not just prompt writing.
Both. Most fixes start by measuring where retrieval breaks: are the right chunks even being retrieved, is the reranker helping, is the eval set realistic. A dedicated Grape5 RAG engineer can instrument your current pipeline, find the failure, and improve it rather than rebuild from zero.
They build an evaluation harness with a golden question set and measure faithfulness, context precision, and answer relevancy, then enforce citations and a refusal when the context is thin. No RAG system is perfect, so the honest goal is measurable grounding and a clear audit trail, not a promise of zero mistakes.
Yes. RAG is model-agnostic, so the same retrieval design works with a hosted provider, a private endpoint, or a self-hosted open model and vector store inside your own environment. Your team sets the data boundaries and the engineer builds within them. Grape5 does not dictate your stack.
You get a pre-vetted, India-based RAG engineer, dedicated to your product for the engagement, with at least four hours of daily overlap with US hours. Grape5 manages and backs the engineer and replaces them for free if the fit is wrong. A typical start is 2 to 3 weeks, and cost is scoped per role and engagement.
Tell us the role. Get vetted profiles.
Send us the seniority and stack you need. We’ll come back with a shortlist of vetted RAG engineers who’ve shipped it, and a plan to start in 2 to 3 weeks.