Hire Prompt Engineers
Who Treat Prompts, Schemas, and Evals Like Product Infrastructure

You can usually start the hiring conversation immediately and receive a shortlist within 24 hours after discovery. For this role, discovery focuses on current prompt workflows, failure cases, output schemas, model providers, tool calls, safety rules, review process, and how prompt behavior affects product or operations. That lets us shortlist Prompt Engineers who can improve behavior measurably instead of people who only write polished prompts.

Yes. You interview shortlisted engineers before committing. We recommend using a real prompt workflow: ask the candidate to improve invalid structured output, design an eval set, reduce hallucination in a grounded assistant, improve a refusal pattern, migrate a prompt to another model, or define safer tool-call instructions. Strong candidates explain how they would test the change and prevent regressions.

The first week should produce a revised prompt workflow or a clear prompt-system diagnosis. You should see before-and-after outputs, test cases, failure analysis, prompt or schema changes, model-specific notes, safety considerations, and rollout guidance. If the engineer only produces nicer wording without evals, versioning, or measurable behavior, the role is not proving production value.

A strong Prompt Engineer should deliver prompt systems that are versioned, tested, structured, reusable, and safer to change. Outcomes should include clearer instructions, better examples, schema adherence, stronger refusal behavior, fewer hallucination failures, safer tool calls, lower review time, and prompt-library governance. The work should be measured through task success, format adherence, regression rate, and stability across model versions.

Quality is managed through role-specific screening, prompt review, eval review, architecture review, code review when prompts live in code, and delivery checkpoints. We look for eval design, structured outputs, function or tool calling, prompt versioning, model migration, safety cases, refusal quality, scoring rubrics, and stakeholder communication. The engineer should treat prompts as production assets, not disposable text.

Yes. The engineer can work with your repositories, prompt management tools, eval tools, model providers, product specs, support transcripts, retrieval sources, tool schemas, CI, issue tracker, and review process. We define the operating model early so prompt versions, test cases, examples, safety rules, brand guidance, schema decisions, and approval paths stay documented for future changes.

Yes. Devlyn plans overlap windows for interviews, product reviews, eval reviews, prompt release discussions, safety reviews, and escalation. For prompt work, overlap matters because product, engineering, support, security, and brand teams may all care about model behavior. We keep the cadence tied to evidence: test cases, outputs, regressions, review time, and rollout risk.

NDA and IP assignment are handled before onboarding. Access is scoped to the prompts, repositories, eval datasets, conversation examples, support logs, model providers, tool schemas, retrieval sources, and environments required for the engagement. Because prompts may contain sensitive business logic, customer context, system instructions, or safety policy, the engineer works within your access controls, privacy rules, audit expectations, and approval process.

Use the risk-free trial to evaluate whether the engineer improves real behavior, designs useful tests, communicates tradeoffs, handles model-specific differences, and avoids brittle prompt hacks. If the fit is wrong, we replace the engineer within 48 hours instead of forcing you through a long notice period or another sourcing cycle.

Yes. You can start with one Prompt Engineer for a prompt audit, model migration, or behavior-improvement sprint. Common additions include an LLM engineer for system architecture, an eval engineer for test infrastructure, a product engineer for integration, a retrieval engineer for grounding issues, or a security engineer for sensitive workflows and jailbreak testing.

Typical options include a prompt audit and improvement sprint, a dedicated senior Prompt Engineer, or a prompt plus LLM plus eval engineering pod. The right model depends on whether you need prompt cleanup, a prompt library, model migration, structured-output reliability, agent tool prompting, hallucination reduction, or ongoing prompt operations. We confirm scope after discovery.

We can support both models. If you already have strong product and engineering leadership, the engineer can plug into your process. If you need more structure, Devlyn can add delivery oversight, eval review, prompt release planning, reporting, and senior technical review. For prompt work, project management is useful when it keeps examples, evals, product requirements, safety policy, and rollout decisions aligned.

Prompt Engineers are hard to screen because the role combines language design, model behavior, evals, structured outputs, tool use, safety, and product judgement. A candidate may produce impressive outputs without knowing how to test regressions. Devlyn reduces the screening burden and gives you a trial structure focused on evidence: can the engineer make a real prompt workflow more reliable and easier to govern?

Devlyn is a better fit when prompt behavior affects production systems, customer workflows, safety, compliance, brand, cost, or long-term maintainability. A freelancer can help with a narrow prompt rewrite, but production prompt systems need evals, versioning, review, replacement support, IP protection, and continuity as models and products change.

This role is best suited for prompt library cleanup, model migration, hallucination reduction, structured-output reliability, agent tool prompts, customer assistant behavior, content generation workflows, support drafting, classification prompts, extraction prompts, refusal quality, brand voice consistency, and eval-driven prompt operations. If the work is mostly model infrastructure, retrieval engineering, or application development, we may recommend a more specialized role.