Post a Prompt Engineer job.
No agency, no placement fee.

Click "Post a job". You can post one job a month for free: it stays open 14 days and is reviewed first if your company website is not verified. Paid plans cover more jobs. Describe the LLM use case specifically: is this a customer-facing assistant, an internal knowledge tool, a RAG pipeline, or an agentic workflow? Name the model stack (GPT-4o, Claude, Llama 3), the orchestration layer if any (LangChain, LlamaIndex), and what quality looks like — hallucination rate, user satisfaction, latency target. That context separates your listing from a generic "prompt engineer wanted" post and attracts engineers who have shipped similar systems. Then publish: the job goes live right away, or after a review if your company website is not verified.

The talent section on this page is pre-filtered for prompt engineering and LLM specialists. Browse profiles showing their headline, core tools, and background. Click any profile to review their LLM projects: the use case built, the model stack used, the evaluation framework designed, and the production results achieved. For prompt engineering roles, prioritize profiles that mention evaluation pipelines, prompt versioning, and RAG architecture. These signals separate engineers who treat prompting as a systematic engineering discipline from those who treat it as ad hoc writing.

GPT-4o, Claude 3.5, Llama 3, Mistral, Gemini, system prompt design, few-shot and chain-of-thought prompting, RAG pipeline design, vector databases (Pinecone, Weaviate, PGVector, Chroma), embedding model selection, prompt versioning and A/B testing, RAGAS evaluation, LangChain, LlamaIndex, LangSmith, OpenAI Assistants API, function calling, AI agents, output validation, content filtering, hallucination detection, and cost-per-query optimization. On the infrastructure side: FastAPI and LLM observability tooling. Specify required versus preferred — strong engineers rarely have depth in every tool, but will have mastered the ones that matter for your stack.

Yes, one job a month is free, with no credit card. It stays open 14 days and is reviewed first if your company website is not verified. Paid plans cover more jobs, and you can also post a single job for $99 without an account. There is no placement fee when you hire.

It depends on the role and on how specific the post is, so we do not promise a delay. Quality improves when your post describes a specific LLM product rather than a generic title. An engineer who has built a production RAG assistant will recognize a matching role. A vague "must know prompt engineering" post attracts a wide range of candidates whose actual fit is hard to assess without significant interview time.

Yes. New profiles are reviewed before being listed on RemoteAI. You see a genuine headline, descriptions of the LLM systems built, the evaluation frameworks designed, the model stacks used, and a work history with real context — not a list of AI buzzwords. Strong profiles describe the specific LLM product, the prompt architecture decisions made, the evaluation approach used, and the production results achieved. Profiles without substantive project context are not listed publicly. Your technical interview should validate evaluation rigor and depth in your specific use case and model stack.

Prompt engineers specialize in the design, evaluation, and optimization of prompts and retrieval pipelines — the layer between the model and the application. LLM engineers have broader scope: they build the full application stack, including APIs, databases, deployment infrastructure, and the LLM integration layer. In practice, these roles overlap. Some companies need a specialist focused entirely on prompt quality and evaluation; others need a full-stack LLM engineer who handles prompting as one part of a larger system. If your biggest challenge is output quality, consistency, and cost optimization — that is a prompt engineer. If your biggest challenge is building the system end-to-end — that is an LLM engineer.

Yes. RemoteAI has prompt engineers from India, the US, UK, Canada, Europe, Brazil, and many other countries; many have worked in remote teams. Prompt engineering work is well-suited to async workflows: evaluation runs, prompt iteration, and documentation are all effectively done in distributed team settings. Many experienced prompt engineers have worked across multiple timezones and are comfortable with async-first communication. If you need timezone overlap for product syncs or daily standups, specify the required window in your post — candidates confirm availability before applying.

The highest-converting prompt engineer posts describe four things clearly: the LLM use case (what the system does and who uses it), the model stack (which models, which orchestration layer), the current state of the system (greenfield or improving an existing product), and the quality bar (how output quality is measured today). For example: "building a customer support assistant on Claude 3.5 — LangChain and Pinecone for RAG, 5,000 conversations per day, we need someone to own prompt quality, the eval framework, and cost optimization end-to-end." That specificity attracts senior engineers and filters out candidates who have only done tutorial-level prompt work.

Free plan: you create a company account and read applications in your RemoteAI dashboard, with messaging and AI matching. One job a month, open 14 days, reviewed first if your website is not verified. The $99 option: no account and no dashboard. Candidates apply to your own email or link, and the listing is reviewed before it goes live.