Offshore LLM app engineers
Hire generative AI engineers who ship LLM features that hold up with real users
Generative AI developers build production LLM features: RAG pipelines, agents, and structured outputs on top of models like GPT, Claude, and open weights. Grape5 gives US teams pre-vetted, dedicated engineers who handle prompts, evals, latency, and cost, backed by our senior engineers and a free replacement if the fit is wrong.
In short
Generative AI developers build production LLM features: RAG pipelines, agents, and structured outputs on top of models like GPT, Claude, and open weights.
Grape5 gives US teams pre-vetted, dedicated engineers who handle prompts, evals, latency, and cost, backed by our senior engineers and a free replacement if the fit is wrong.
When to hire generative AI developers
- You have thousands of support tickets, docs, and PDFs and want a retrieval assistant that answers from your own content with citations, not a generic chatbot.
- You want to automate a multi-step internal workflow like triage, drafting, or data entry with an agent that calls your tools and knows when to hand off to a human.
- You need to pull structured fields out of messy documents like invoices, contracts, or resumes and get clean, validated JSON your systems can trust.
- You already shipped an LLM feature, but it is slow, expensive, or unpredictable, and you need someone to add evals, caching, and model routing to make it production-ready.
How we vet generative AI developers
Every engineer we put forward is screened by a senior Grape5 engineer before you meet them. For generative AI developers, we look specifically at:
- RAG that retrieves the right thing: we check how they chunk and embed documents, whether they rerank, and how they measure retrieval quality instead of eyeballing a few queries.
- Evals before vibes: we look for engineers who build a labeled eval set, run regression checks when prompts or models change, and know where LLM-as-judge scoring misleads.
- Reliable structured output: function and tool calling, JSON schema enforcement, and sane handling of malformed responses, timeouts, and partial failures instead of hoping the model behaves.
- Cost and latency control: token accounting, streaming, prompt caching, batching, and model routing so a feature does not get slow or expensive at scale.
- Grounding and safety: reducing hallucination with retrieval and citations, plus guarding against prompt injection and keeping PII out of prompts and logs.
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
Yes. Most generative AI work is model-agnostic. A strong engineer moves between hosted APIs like OpenAI and Anthropic and open-weight models, and fits into your existing backend, vector store, and cloud rather than forcing a rewrite. Grape5 matches engineers to your specific stack before you commit.
We vet for the habits that matter: grounding answers in retrieved sources with citations, building eval sets to catch regressions, and validating outputs instead of trusting them. No one can make an LLM perfect, so we look for engineers who measure quality and design around failure, not who promise it away.
A fair concern. We screen for engineers who keep PII out of prompts and logs, understand provider data-retention settings, and defend against prompt injection when an app takes untrusted input. They work dedicated to your product, under whatever access controls and policies you set for the project.
Yes. Classic ML engineers train and serve models; LLM app developers build products on top of existing models: RAG, agents, prompts, evals, and the plumbing around them. It overlaps with backend work, but the hard parts are non-deterministic output, eval design, and cost and latency tradeoffs a typical backend dev has not faced. Tell us the split you need and we match accordingly.
A typical engagement starts in 2 to 3 weeks once we understand the role. Your engineer is dedicated to your product with at least 4 hours of daily overlap with US working hours. If the fit is wrong, Grape5 replaces them for free. You are not left managing a freelancer who disappears.
Tell us the role. Get vetted profiles.
Send us the seniority and stack you need. We’ll come back with a shortlist of vetted generative AI developers who’ve shipped it, and a plan to start in 2 to 3 weeks.