Hire senior prompt engineers who measure what their prompts do
Senior prompt engineers who design prompts, tool definitions and evaluations for production LLM features, and treat prompts as code with tests and versions.
By the Ryz Labs team · Updated October 2026
Hiring prompt engineers through Ryz gets you senior engineers who treat prompts as production code: designed for a task, tested against real cases and versioned so changes do not quietly break things. They are top 1% of the candidates we interview, they join your team or work as an AI pod in your stack, and they work on US business hours.
What our prompt engineers work on
Our prompt engineers are software engineers who specialize in getting reliable behavior from large language models. They work with Anthropic and OpenAI models, open-weight models, structured output and tool-calling APIs, and evaluation tools such as promptfoo, LangSmith, Braintrust or custom test harnesses in Python or TypeScript. Typical projects:
- Turning a promising demo prompt into a feature that holds up on thousands of real inputs.
- Building evaluation suites with labeled cases, rubric graders and regression checks that run in CI.
- Designing tool definitions and system prompts for agents that call internal APIs, search and databases.
- Structured extraction prompts with JSON schemas, validation and retry strategies.
- Model migrations: moving a feature to a newer or cheaper model and proving quality did not drop.
- Cost and latency work: prompt caching, shorter contexts, routing easy cases to smaller models.
- Guardrails against prompt injection, data leakage and off-topic responses.
Skills we vet for
- Task decomposition. Breaking a fuzzy goal into steps, deciding what the model should do and what plain code should do.
- Prompt structure. Clear instructions, role and context, examples chosen to cover edge cases, and output formats that are easy to parse.
- Evaluation design. Building representative test sets, writing rubrics, calibrating LLM-as-judge graders against human labels and tracking pass rates over time.
- Retrieval context. How chunking, ranking and context ordering affect answers, and how to make the model cite its sources.
- Tool use and agents. Tool schemas, error messages that help the model recover, step limits and avoiding loops.
- Security. Direct and indirect prompt injection, separating trusted and untrusted content, and limiting what tools can do.
- Model knowledge. Differences between model families, context windows, reasoning modes and how sampling settings change behavior.
- Engineering discipline. Prompts in version control, templating, logging inputs and outputs safely, and A/B testing changes in production.
How we vet prompt engineers
Prompt engineering is easy to claim and hard to prove, so our recruiters look for engineers who can show evaluations, not just clever prompts. Our in-house ARC system ranks the pipeline, and candidates complete structured NTRVSTA AI interviews where they reason through failure cases and evaluation design. Recruiters review every candidate before and after, then send a curated shortlist. AI scores are advisory, and humans make the decisions.
Sample interview topics
- A support assistant answers correctly most of the time but invents policy details in a minority of cases. How do you measure the problem and reduce it?
- Design an evaluation for a feature that drafts sales emails. What do you grade automatically, and what needs human review?
- A user pastes a document containing hidden instructions to email data externally. How should the system and its tools be designed to resist that?
- You need to move a high-volume extraction prompt to a cheaper model. Walk through how you would prove quality is acceptable.
- An agent sometimes calls the same tool repeatedly without progress. What would you change in the tool definitions, prompts and code?
Ways to hire prompt engineers
| Option | Best for | Trade-offs |
|---|
| Freelance marketplace | Drafting a few prompts for an internal tool | Without evaluations and ownership, prompts drift as models and inputs change. |
| Staffing or recruiting agency | Filling a role with a new title | The role is new and loosely defined. Agencies often cannot tell strong candidates from people who only experiment. |
| In-house recruiting | A permanent applied AI team | Slow, and assessing evaluation skill needs someone who has shipped LLM features. |
| Ryz Labs staff augmentation | Adding a senior prompt engineer to an existing product or AI team | You own the product decisions. Best when engineers can integrate their work into your codebase. |
| Ryz Labs AI pod team | Building a complete LLM feature or agent in your stack | A dedicated pod with a tech lead, ML and backend engineers. Scoped as a team with a plan up front. |
If you only need someone to write marketing copy with a chatbot, or you want a ready-made AI product to license, Ryz is not the right fit. Our prompt engineers build production features with your engineers.
Why hire prompt engineers from Latin America
Prompt work moves in short loops: change the prompt, run the evals, look at the failures with a domain expert, change it again. Engineers on your hours can run those loops with your product managers and subject experts several times a day rather than once per overnight cycle.
Many senior engineers in the region came to LLM work from backend, data or NLP roles, which is the right background. They write precise English instructions, which matters when the instructions are the product, and many can also test behavior in Spanish and Portuguese for multilingual users.
Related roles
FAQ
Is a prompt engineer different from an LLM engineer?
There is a lot of overlap. Prompt engineers focus on model behavior, prompts, tools and evaluation, while LLM engineers also own retrieval infrastructure, serving and integration. Many of our engineers do both, and we match the profile to your gaps.
Which models do your prompt engineers work with?
Mostly Anthropic and OpenAI models, plus open-weight models where cost or data residency matters. They test across models instead of assuming one is best.
How much does it cost?
$7,000 to $15,000 per engineer per month, scoped per team. You get a scoped plan, a price and the names of the people who would do the work before you sign.
What hours do they keep?
They work on US business hours, including New York hours. Ryz engineers work on your team, reporting to your leads. Talk to us to scope your team.
Questions we didn't answer? Email info@ryzlabs.com.