Hire LLM engineers in Latin America

What an LLM engineer does

• Builds retrieval over your documents and data, so answers come from your own content.
• Designs prompts, tools and agent steps, and keeps them under version control.
• Builds evaluation: test sets, graded examples and regression checks that run before every release.
• Watches cost and latency, and picks the smallest model that does the job well.
• Adds guardrails against prompt injection, leaks of private data and unsafe output.
• Connects all of it to your backend, with logging and monitoring.

Skills to look for

• Strong Python or TypeScript, and solid backend habits: APIs, queues, tests.
• Experience with hosted model APIs and with at least one open model.
• Retrieval and search: embeddings, vector databases, chunking and reranking.
• Evaluation methods that go beyond reading a few answers: labeled sets, pairwise comparison, human review.
• Observability for model calls, so prompts, traces and outputs can be inspected later.
• Awareness of security around prompts, tools and user data.

Interview questions that show real experience

• Tell me about an LLM feature you shipped. How did you know it was good enough to launch?
• A user reports a wrong answer. Walk me through how you find the cause.
• How would you cut the cost of this feature in half without users noticing?
• When would you fine-tune a model instead of improving retrieval or prompts?
• How do you stop a user from making the model ignore its instructions?

Good answers mention what they measured, what failed and which trade-offs they chose. General talk about prompt engineering with no examples is a warning sign.

LLM engineer, ML engineer or data scientist?

An LLM engineer works mostly on top of existing models and spends the day on retrieval, evaluation and product integration. A machine learning engineer trains and ships models on your own data. A data scientist answers business questions with data, analyses and experiments. If your roadmap is chat, search, summaries or agents over your own content, you want an LLM engineer. For the other roles, see hiring AI and ML engineers in Latin America.

Why Latin America for LLM work

LLM features keep changing after launch, and improving them needs short feedback loops with product and support. Engineers in Mexico, Peru, Chile, Argentina and Brazil work within a few hours of US time, so they are online when your users and your team are.

How we help

Tell us about the role. We speak with each engineer about it before we share their profile, and you receive a shortlist of vetted engineers. You only pay when you hire, and if the hire leaves within 90 days, we find a replacement for free.

Questions companies ask

Does an LLM engineer need a machine learning background?

It helps. For most product work, strong software engineering and real experience shipping LLM features matter more.

Can the engineer work as a contractor?

Yes. We recruit for contractor and full-time roles.

Do I pay anything if I do not hire?

No. You pay only when you hire someone we introduced.