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Hire LLM engineers in LatAm

Hire LLM Engineers Who Ship Production GenAI

Prompt engineering is a skill, not a role. We place senior LLM engineers who build RAG pipelines, agents, fine-tunes, and the evaluation infrastructure around them.

Book a free call → See the LLM stack we screen for

Why hire a dedicated LLM engineer

RAG and agents that actually work

Retrieval tuned on your data, agents with real tool use, not another hallucination demo.

Evaluations and observability

If you cannot measure LLM quality, you cannot improve it. These engineers build the evals first, then iterate.

Cost and latency under control

Model choice, caching, batching, streaming, cost per request tracked and tuned. Not a surprise invoice.

What a LLM engineer does

Design and build RAG pipelines with real embedding + retrieval strategy

Ship agent architectures with structured outputs and tool use

Build evaluation harnesses (offline + online), red-team prompts, track regressions

Fine-tune when it matters, use frontier APIs when it does not

Monitor LLM quality, latency, cost and user feedback in production

Partner with product on where AI genuinely adds value vs where it is noise

PythonTypeScriptLangChainLlamaIndexpgvectorPineconeOpenAIAnthropicMistralvLLM

We test for the bar, not the buzzwords

Python & TypeScript. Both languages fluent, with Python owning the eval and training surface.

RAG architecture. Chunking strategy, embedding model choice, retrieval tuning, reranking, hybrid search.

Agent frameworks. Structured outputs, tool use, state machines, LangGraph, OpenAI function calling.

Evaluation. Offline eval harnesses, LLM-as-judge, regression suites, human review loops.

Production ops. Streaming, caching, cost tracking, latency budgets, model-fallback strategies.

The risk is on us, not you

Pre-vetted shortlists

3-5 people who fit the exact role, not a résumé dump.

Tested on a real RAG build

We screen LLM engineers on a real RAG task plus an evaluation harness, not a prompt quiz.

98% + 3-month guarantee

Not a fit? We replace at no cost.

Founder-led

Operators who’ve made this hire, not account managers.

From first call to hired in ~3 weeks

Discovery call

We start with a 30-minute call. You tell me the role, the stack, and what “great” looks like. No charge, no obligation.

Pre-vetted shortlist

We screen against the role-specific bar and send 3-5 people in days, not a résumé dump.

You interview & choose

Meet your favorites. We coordinate everything. Most clients hire within ~3 weeks of the first call.

Onboard + guarantee

We handle contracts and payroll via Talent Management (COR). Not a fit in 90 days? We replace at no cost.

Common mistakes when hiring LLM engineers

LLM hiring is where the most hype-vs-reality gap lives. Four traps founders fall into.

Confusing prompt engineering with LLM engineering

Prompt engineering is a skill inside a role, not a role. Hire for architecture, retrieval, and evaluation thinking. Prompts are the last 10 percent, not the first 90.

Skipping the evaluation requirement

If a candidate cannot describe how they would measure whether their LLM feature is getting better or worse, they have not shipped to real users. Evals are the price of entry.

Picking framework over architecture

LangChain or LlamaIndex expertise matters less than retrieval architecture, embedding model choice, and agent design. Screen for the fundamentals, not for the library.

Underweighting cost awareness

Model choice, caching, batching, context-window strategy. Every one of these is a cost lever. Great LLM engineers can draw your cost curve on a napkin. Juniors send you a surprise invoice.

Hiring LLM engineers, your questions

LLM engineer vs AI engineer, what's the difference?

Overlap is high. An LLM engineer specializes in language-model systems (RAG, agents, prompts, fine-tuning). An AI engineer is broader and typically owns the full product around the AI. If your product is primarily a chat, retrieval, or agent experience, LLM engineer is the sharper hire.

Do your LLM engineers work with open-weights models (Llama, Mistral)?

Yes. Depending on your product requirements and cost structure, they will self-host open-weights via vLLM or Together, or stay on frontier APIs. They have strong opinions on which to use when.

Can they build evaluations?

Yes, this is table stakes for a senior LLM hire. Offline eval harnesses, LLM-as-judge, human review loops, and regression suites are a required part of the screen.

What LLM frameworks do they know?

LangChain, LlamaIndex, LangGraph, OpenAI's SDKs, Anthropic's SDKs, pgvector, Pinecone, Weaviate. Framework fluency matters less than architecture fluency, we screen for both.

Should I hire an LLM engineer or an ML engineer?

Different roles. LLM engineers build on existing models; ML engineers train and deploy custom models. For most startups the LLM engineer is the right first AI hire.

Can they ship to production, or just prototype?

Ship. Our screen specifically tests for evals, cost awareness and production hardening, which filters out demo-only candidates.

What does a senior LLM engineer in LatAm cost?

Typically 50% below US rates for equivalent seniority. See the Compensation Report or book a call for specifics.

Can I hire an LLM engineer part-time or on contract?

Yes. Direct-hire, contract-to-hire, or embedded contractor, we structure around how you want to work. Start with a call.