We place pre-vetted, remote prompt engineers on your team to design, evaluate and version the prompts behind your AI features and workflows. They work US hours, report to you, and come with a 12-month replacement guarantee.
Prompts quietly run more of your business than most teams realize: the support bot's tone, the lead scorer's judgment, the call summary your reps read before every meeting. Yet most were written once by whoever had time, patched in production when something broke, and never tested against the cases that broke them last month.
When you hire a prompt engineer through RevHires, you get someone who treats prompts as product. They build test sets from real inputs, compare versions side by side, track scores per model, and only ship changes that measurably improve output. They document why each instruction exists, so the next model upgrade is a planned migration instead of a surprise.
A prompt engineer, sometimes called an LLM prompt specialist, AI prompt engineer or AI behavior designer, designs the instructions, examples and context that steer large language models like GPT, Claude and Gemini. They write system prompts, structure few-shot examples, define output formats, and build evaluation sets that prove one version beats another. The strongest prompt engineers work close to AI engineering: they call model APIs, version prompts in Git and run automated evals, rather than just chatting with a model in a browser.
Writes system prompts that define role, scope, tone, policies and output format for each AI feature. Structures them with clear sections and delimiters, removes conflicting instructions, and keeps them as short as quality allows so they stay maintainable and inexpensive to run.
Collects real inputs from logs, tickets and edge cases, writes expected outputs or grading rubrics, and tags each example by difficulty and category. The test set becomes the source of truth for whether a prompt change helped, and it grows every time a new failure shows up.
Runs competing prompt versions across the full test set with tools like Promptfoo or the OpenAI and Anthropic consoles. Compares accuracy, format compliance, length and cost, then ships the winner with a written changelog explaining what changed and why it performed better.
Defines JSON schemas and uses structured output and tool calling features so downstream code and automations receive clean, parseable data every time. Handles nulls, enums and validation rules explicitly, so an unexpected input does not break the workflow that depends on the response.
Selects and rewrites examples that teach the model your exact standards, from brand voice to classification boundaries. Decides what reference content belongs in context, trims what dilutes attention, and works with engineers on retrieval so the model sees the right facts at the right time.
When a provider releases a new model or retires an old one, reruns the full eval suite, documents behavior differences and adjusts prompts before the switch. Your AI features keep working through upgrades instead of quietly degrading the week a model changes underneath them.
Probes prompts with jailbreak attempts, prompt injection hidden in user content, off-topic requests and adversarial inputs. Hardens instructions, adds refusal and escalation behavior, and documents remaining risks so your product, legal and support teams know exactly what the AI will and will not do.
Stores prompts in Git or a prompt management tool like LangSmith or Langfuse, with version history, owners and linked eval results. Writes reusable templates and a style guide so other teams build on tested patterns instead of starting from a blank text box.
Every profile includes a voice clip, enterprise background, English level and a scored role play.
Prompt engineer candidates get a broken production prompt, a 40-example test set and two hours. They must diagnose the failures, rewrite the prompt, enforce a JSON schema and report before and after scores. A live session follows where they defend each instruction and red-team their own prompt in front of us. Because writing is the job, we grade English precision closely, then confirm work history through references.
The US number is Indeed's average base salary for prompt engineers, drawn from a small sample of salaries, so treat it as directional. Glassdoor reports higher figures once bonuses are counted as total pay. Adding 30% for payroll tax and benefits gives about $12,190 per month. Through RevHires, a full-time hire in this role typically earns $15 to $35 per hour. The RevHires figure uses the top of that range, $35 per hour at 40 hours, paid direct to your hire or through RevHires, plus one flat placement fee. Want the team managed for you? Done-for-you pricing is covered on your strategy call.
Role scorecard, stack, salary band and must-haves.
Targeted search across Latin America, South Africa and Egypt.
Language, enterprise background and a role test.
Vetted finalists with recorded intros.
Your interviews, your offer, 12-month guarantee.
One flat placement fee, due only when your hire accepts. 12-month replacement guarantee. Pay your hire direct, or through RevHires and we handle payroll.
Through RevHires, a full-time prompt engineer typically earns $15 to $35 per hour, about $2,600 to $6,070 per month, paid direct to your hire or through RevHires. You pay one flat placement fee, and only when you hire. Want the team managed for you? Done-for-you pricing is covered on your strategy call.
After a discovery call covering your models, use cases and current prompt problems, we run a custom search and deliver a shortlist of 2 to 3 candidates within 48 to 72 hours. Every candidate has already fixed a broken production prompt against a real test set in our screening, so your interviews can focus on your domain and team fit.
Writing precision is the skill we screen hardest for this role. Candidates are college-educated with near-native English, and many have worked at global companies like Google, Microsoft or Amazon. In screening, every instruction they write is reviewed for ambiguity, and their output is judged against what a US customer would expect to read, including tone and idiom.
US business hours. That overlap matters when a prompt change goes live and your support team needs answers immediately. Depending on the market you hire from, your hire is 0 to 10 hours from US time zones.
Your team manages them directly, usually under a product or engineering lead. We recommend judging the role on numbers: eval scores per feature, production failure rate, and how quickly prompts are validated after a model change. Ask that every prompt change ships with before and after results, the same way code ships with tests.
Every engagement includes an NDA and documented data handling protocols, and RevHires signs BAAs for healthcare clients. We recommend building test sets from anonymized or redacted records, using enterprise data controls from your model providers, and keeping all work inside your own accounts with access you can revoke. Your company owns every prompt and evaluation set.
Every placement includes a 12-month replacement guarantee. If the hire is not working out within that window, tell us and we run a replacement search under your 12-month guarantee. To reduce the odds of a mismatch, we use discovery to separate prompt engineering needs from broader AI engineering needs, which call for different profiles.
See the shortlist, hear the voice clips, and get the right hire in front of your buyers fast.