We place pre-vetted, remote AI engineers directly on your team. They build retrieval pipelines, agents and evaluation suites in Python on the OpenAI and Anthropic APIs, work US hours and come with a 12-month replacement guarantee.
If you have tried to hire an AI engineer, you have met the candidates who wrap an API call in a Streamlit app and call it a product. It looks great in the meeting. Then real users ask questions the retrieval misses, latency spikes, costs triple, and nobody can tell whether last week's prompt change made answers better or worse.
Production AI engineering is mostly the unglamorous part: chunking and indexing strategy, evaluation sets, tracing, guardrails, fallbacks and cost control. RevHires screens for exactly that. Every engineer we place has built and shipped LLM features in Python, can explain their retrieval and evaluation choices, and works your hours alongside your existing developers.
An AI engineer, also called an LLM engineer, generative AI engineer or applied AI engineer, builds software features powered by large language models. They design retrieval augmented generation (RAG) pipelines over your documents, build tool-using agents, integrate models from OpenAI, Anthropic and open-weight providers, and wrap it all in APIs your product can call. Unlike a machine learning researcher, they rarely train models from scratch. Their job is to make existing models accurate, fast, safe and affordable inside a real application.
Builds ingestion that parses PDFs, help articles and database records, chunks them sensibly, embeds them and stores them in pgvector, Pinecone or Weaviate. Tunes hybrid search, metadata filters and reranking until retrieval reliably returns the passage that actually answers the question.
Designs agents that call your internal APIs, query databases and take actions, using native function calling or frameworks like LangGraph. Sets clear tool schemas, step limits and human approval points so an agent cannot loop forever or take an irreversible action unsupervised.
Creates labeled test sets from real user questions and scores outputs for correctness, groundedness and format with automated graders plus human spot checks. Runs evals in CI on every prompt or model change, so regressions are caught before release instead of reported by customers.
Benchmarks OpenAI, Anthropic and open-weight models on your actual tasks for quality, latency and price. Routes simple requests to smaller models and hard ones to stronger models, and keeps a fallback provider configured so one vendor outage does not take your feature down.
Wraps AI logic in FastAPI or Node.js services with streaming responses, authentication, rate limiting and queues for long-running jobs. Containerizes with Docker and deploys to AWS, GCP or Azure through your existing CI/CD pipeline, held to the same code review standards as the rest of your engineering team.
Instruments every request with LangSmith, Langfuse or OpenTelemetry so you can see the prompt, retrieved context, tool calls, tokens and latency behind any answer. Uses those traces to debug bad outputs and builds dashboards for cost per request and error rates.
Adds input checks for prompt injection, output validation against schemas, PII redaction and permission filters so users only retrieve documents they are allowed to see. Defines refusal and escalation behavior for out-of-scope requests and documents known limitations for your product team.
Cuts token spend with prompt caching, tighter context, batching and smaller models wherever quality holds. Reduces latency with streaming, parallel tool calls and precomputed embeddings. Reports cost per user action so product decisions are made with real unit economics, not guesses.
Every profile includes a voice clip, enterprise background, English level and a scored role play.
AI engineer candidates complete a take-home RAG build over a real document set, with a required evaluation script, then defend it in a live technical review. We probe chunking choices, failure cases, latency and cost. A second round covers system design for an agent with tool access, plus a debugging exercise on a broken trace. We finish with written English checks and references from past engineering leads before anyone reaches your shortlist.
The US figure is the BLS median for software developers, which covers all developers, not just AI specialists. Engineers with shipped LLM experience may earn above that median, so treat it as a conservative baseline. Adding 30% for payroll tax and benefits gives about $14,520 per month, before recruiter fees, equity or equipment. 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 AI 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 where we pin down your stack, use case and seniority, we run a custom search and deliver a shortlist of 2 to 3 candidates within 48 to 72 hours. Every candidate on it has passed our take-home RAG build and live technical review, so your engineering team spends interview time on fit and final decisions, not basic screening.
Yes. Candidates are college-educated with near-native English, and many have backgrounds at companies like Microsoft, Google, Amazon or Oracle. They work in your Git workflow, write clear pull request descriptions and design docs, and join your sprint rituals. We test written technical communication during screening because async clarity matters as much as code quality.
Placed engineers work US business hours, giving you full overlap for standups, pairing, code review and incident response. Depending on the market you hire from, your hire is 0 to 10 hours from US time zones. If you want some early coverage for deploys or batch jobs before your US team logs on, we can screen for a split schedule during discovery.
You do. Talent placement is a direct hire who reports to your engineering lead. We recommend holding them to the same standards as any engineer, plus AI-specific ones: eval results attached to every pull request that changes a prompt or model, tracing in production, and tracked metrics for answer accuracy, latency and cost per request.
Every engagement includes an NDA and documented data handling protocols, and RevHires signs BAAs for healthcare clients. We recommend least-privilege access, provider API keys kept in a secrets manager, and the enterprise data controls OpenAI and Anthropic offer to eligible customers. Your company owns all code, prompts and evaluation sets the engineer produces.
Each placement carries a 12-month replacement guarantee. If the engineer is not working out within that window, tell us and we run a replacement search under your 12-month guarantee. Most mismatches come from unclear scope, such as customer-facing RAG versus internal tooling, so we define the first project in detail during discovery.
See the shortlist, hear the voice clips, and get the right hire in front of your buyers fast.