Hire senior LLM engineers who get models to production

Hiring LLM engineers through Ryz gets you senior Latin American engineers who treat large language models as components in a reliable system. They are vetted, top 1% of the candidates we interview, and they embed in your team, working in your repos and cloud within an hour of US time zones.

What our LLM engineers work on

Our LLM engineers build the layer where language models meet your data and your users. That means prompts under version control, retrieval that respects permissions, tool calls with typed contracts, and evaluation that runs on every change. Typical projects:

• Question answering over policies, contracts and knowledge bases, with citations back to source passages.
• Structured extraction from messy documents into JSON schemas, validated with Pydantic or Zod and checked against ground truth.
• Multi-step agents built with LangGraph, the OpenAI Agents SDK or Anthropic tool use, with bounded tools and human approval steps.
• Fine-tuning or LoRA adaptation of open-weight models such as Llama or Mistral, served with vLLM, when an API model is too costly or cannot be used.
• Content review and compliance checks that flag issues in marketing or customer communications for a human reviewer.
• Model gateways that route between providers, cache prompts, enforce budgets and log every request for debugging.

Skills we vet for

• Prompt and context design. System prompts, few-shot examples, context ordering and keeping long contexts relevant rather than just large.
• Structured outputs and tool use. JSON schema design, function calling, handling partial or malformed outputs and retry strategies.
• Retrieval pipelines. Embeddings, chunking, hybrid search, reranking and query rewriting, with measurement at each stage.
• Evaluation. Golden sets, pairwise comparisons, LLM-as-judge with calibration against human labels, and evals wired into CI.
• Fine-tuning tradeoffs. When supervised fine-tuning, LoRA or distillation beats prompting, and how to build the training data.
• Inference and cost. Token budgets, streaming, prompt caching, batching and serving open models with vLLM or TGI.
• Safety and guardrails. Prompt injection defenses, output filtering, PII handling and scoping what an agent can touch.
• Observability. Tracing chains and agent runs with Langfuse, LangSmith or OpenTelemetry so failures can be replayed.

How we vet LLM engineers

Recruiters source engineers who have shipped LLM features to real users, then our in-house ARC system ranks the pipeline. Candidates take structured NTRVSTA AI interviews built around LLM system design and failure analysis. Recruiters review each candidate before and after that interview and assemble a curated shortlist for you. AI scores inform the process but do not decide it. People do.

Sample interview topics

• A user pastes text that tells your assistant to ignore its instructions and email a document. How do you design the tools and prompts so that cannot cause harm?
• Your extraction prompt works on 95 sample documents and fails on scanned faxes. How do you diagnose it and decide between OCR fixes, prompt changes or a different model?
• How would you calibrate an LLM judge so its scores agree with your domain experts, and how would you know when it drifts?
• Compare long-context prompting with retrieval for a 2,000-page manual. What would you measure to choose?
• Walk through fine-tuning a small open model for classification. How do you build the dataset and prove it beats the prompted baseline?

Ways to hire LLM engineers

We are not the right choice if you want a licensed AI platform rather than engineers who build in your stack, or hourly gig work with no conversation first. Teams that need coverage in European or Asian time zones should look at global networks.

Why hire LLM engineers from Latin America

LLM work is iterative and visible. Stakeholders read outputs, disagree with them and want changes quickly. When your LLM engineers work the same hours as your product, legal and support teams, a bad answer reported at 10 a.m. can become a new eval case and a fix by the end of the day.

Senior engineers in Latin America have spent years building backend systems for US companies, and that background shows in LLM work, which is mostly APIs, data pipelines and reliability. They work in English and are comfortable presenting model behavior and tradeoffs to business owners.

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FAQ

Do your LLM engineers work with both API models and open-weight models?

Yes. Most production work uses Anthropic and OpenAI models through their APIs or Azure. Engineers also fine-tune and serve open-weight models when cost, latency or data residency calls for it.

How do you keep LLM features from regressing?

We vet for evaluation discipline. Our engineers build test sets early, run them in CI and compare results whenever a prompt, retrieval setting or model version changes.

How is pricing set?

Every engagement gets a custom quote, scoped per team. You receive a plan, a price and the names of the people who would do the work before signing.

What is the contracting setup, and do they overlap with our hours?

You sign one contract with Ryz. Our engineers work with us as independent contractors, and we handle paying them. They work within an hour of US time zones, so your workday overlaps fully.

Questions we didn't answer? Email info@ryzlabs.com.