LLM and RAG engineers
Hire LLM engineers who ship RAG and agents that hold up in production
LLM engineers build production features on top of large language models: RAG pipelines, agents, structured extraction, and evals that catch regressions before users do. Grape5 gives US teams pre-vetted, India-based LLM engineers, dedicated to your product, with at least 4 hours of daily US overlap and a typical start in 2 to 3 weeks.
In short
LLM engineers build production features on top of large language models: RAG pipelines, agents, structured extraction, and evals that catch regressions before users do.
Grape5 gives US teams pre-vetted, India-based LLM engineers, dedicated to your product, with at least 4 hours of daily US overlap and a typical start in 2 to 3 weeks.
When to hire LLM engineers
- You are building a RAG assistant over your docs, tickets, and knowledge base, and you need retrieval good enough that answers cite the right source instead of confidently making things up.
- Your GPT-4 or Claude prototype demos well but falls apart on real inputs, and you need production hardening: eval sets, structured output, retries, guardrails, and cost controls before it goes live.
- You are building an agent that calls your internal APIs and tools, and you need someone who can control the loop, handle tool errors, and stop it from burning tokens or taking unsafe actions.
- You need to pull structured data from messy documents like contracts, invoices, or resumes at scale, with schema validation, confidence scoring, and a human-review fallback when the model is unsure.
How we vet LLM engineers
Every engineer we put forward is screened by a senior Grape5 engineer before you meet them. For LLM engineers, we look specifically at:
- RAG retrieval quality: how they chunk, which embedding model they pick and why, whether they add reranking (Cohere rerank, cross-encoders) and hybrid search (BM25 plus dense), and how they measure it with context precision and recall instead of eyeballing three questions. We hand them a pipeline returning irrelevant chunks and watch them debug it.
- Evals over vibes: whether they build eval sets and an LLM-as-judge harness, run regression checks when a prompt changes, and keep a golden dataset, or just tweak prompts blind and hope nothing downstream broke.
- Structured output reliability: function calling and JSON schema or constrained decoding, Pydantic or Zod validation, and a repair-and-retry path for malformed output that would otherwise crash the code parsing it.
- Cost and latency control: token budgeting, prompt caching, streaming, routing easy cases to a cheaper model, and capping agent loop iterations so a runaway loop does not silently blow the monthly bill.
- Prompt injection and safety: keeping system instructions separate from user data, sanitizing tool and retrieval output, and not letting raw model output reach a database, shell, or payment call without checks.
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
We test the craft, not the buzzwords. Senior Grape5 engineers run a live session: debugging a RAG pipeline that returns irrelevant chunks, hardening a prompt against an eval set, and making model output parse reliably every time. We also check system design and communication. Calling an API is easy; building something that survives real users and stays accurate over a real corpus is not, and that is what we screen for.
That gap is exactly what we screen for. A demo that answers three curated questions is not a system that stays accurate as the corpus grows and documents go stale. We look for engineers who measure retrieval quality, add reranking and hybrid search when it helps, handle chunking and freshness, and write evals so a prompt change does not quietly regress. Candidates who only build the happy path do not pass.
Both matter, and we check that they understand what the framework is doing underneath. Tools like LangChain or LlamaIndex speed up early work, but production teams often drop to direct provider SDK calls for control over cost, latency, and error handling. We prefer engineers who can use the frameworks and also explain the tradeoff, not ones who are stuck the moment the abstraction leaks.
Engineers are India-based, dedicated to your product, and managed and backed by Grape5, and they work inside the access you grant: your repos, your cloud, your model provider accounts, under NDA. We will not overstate a certification we do not have. Your security posture depends on how you scope access, and the engineer works within whatever key handling, permissions, and data rules you set.
You are not stuck with a bad match. If the fit is wrong, Grape5 replaces the engineer for free. Because the engineer is dedicated to your product and backed by us, you are not on your own the way you are with a freelancer who goes quiet or a marketplace that hands you a resume and disappears. A typical engagement starts in 2 to 3 weeks, with at least 4 hours of daily overlap with US hours.
Tell us the role. Get vetted profiles.
Send us the seniority and stack you need. We’ll come back with a shortlist of vetted LLM engineers who’ve shipped it, and a plan to start in 2 to 3 weeks.