Post an LLM Engineer job.
Hire in days, not months.

Click "Post a job — it's free" — no credit card or subscription required. Describe the specific LLM use case you are building: a RAG system, a customer-facing chat assistant, an agentic workflow, or a fine-tuning pipeline. Name the model stack (GPT-4o, Claude, Llama 3, Mistral), the orchestration layer (LangChain, LlamaIndex, direct API), and any production constraints like latency targets or cost-per-query limits. These details separate your listing from generic "LLM engineer wanted" posts and attract engineers who have actually shipped similar systems — not just prototyped them.

The talent section on this page is pre-filtered for LLM, NLP, and prompt engineering specialists. Browse profiles showing headline, core skills, and a description of their GenAI work. Click any profile to review their RAG projects, LLM applications they have shipped, evaluation frameworks they have built, and any open-source contributions. Filter by experience level and availability. For LLM roles, look specifically for profiles that mention production deployments, token cost optimization, or evaluation pipelines — these are signals that the candidate has moved beyond the prototyping phase and understands what it takes to ship reliable LLM products.

LangChain, LlamaIndex, LangGraph, RAG architecture, vector databases (Pinecone, Weaviate, PGVector, Chroma), embedding models, fine-tuning (LoRA, QLoRA, RLHF), GPT-4o, Claude 3.5, Llama 3, Mistral, Gemini, prompt engineering, RAGAS evaluation, OpenAI Assistants API, function calling, AI agents, and multi-agent orchestration with CrewAI or AutoGen. On the infrastructure side: FastAPI, streaming responses, token cost tracking, and LLM observability tools like LangSmith or Arize. Specify which are required versus nice-to-have — LLM engineering is evolving fast and strong engineers will not have everything.

Yes — entirely free. No credit card, no subscription, no placement fee if you hire. RemoteAI is free for companies across the board: posting jobs, browsing LLM engineer profiles, sending messages, managing applications, and scheduling interviews are all included with zero cost. There is no employer-side paywall or premium tier. You can post your LLM role today and start receiving applications within 24–48 hours without spending anything. The platform earns on the talent side — employers are never charged.

Most LLM job posts receive their first applications within 24–48 hours. The LLM talent market moves quickly — strong engineers are actively looking and check new postings regularly. However, competition is real: a generic "LLM engineer wanted" post will underperform a specific one every time. Posts that name the model stack, describe the problem, and set a compensation range consistently attract senior engineers faster. If you are hiring for a production RAG system or an agentic workflow, say so explicitly — those details resonate with exactly the engineers you want.

Yes. Every profile is reviewed before appearing on RemoteAI. You see a genuine headline, a description of the LLM systems and products they have built, specific tools and model stacks used, and a work history with context — not a list of buzzwords. Strong profiles describe the problem, the architectural choices made, and the results achieved. Profiles without real project substance are not listed publicly. That said, LLM engineering is a field where domain-specific judgment matters enormously — always conduct your own technical interview focused on evaluation design, failure modes, and production experience.

This is a common and very reasonable requirement. Many LLM engineers on RemoteAI combine generative AI experience with strong Python backend skills — FastAPI, async services, streaming, cloud deployment, and API design. When posting, list both dimensions clearly: the LLM work (RAG, fine-tuning, agents) and the backend requirements (API layer, authentication, database integration, deployment). This helps candidates self-qualify based on their actual experience. Engineers who only do LLM prototyping will opt out, while full-stack AI engineers who ship production systems will apply. Specificity here always improves match quality.

Yes. RemoteAI's LLM engineers are located across India, the US, UK, Canada, Europe, and 40+ additional countries — all working remotely and experienced with distributed team collaboration. LLM engineering is one of the most globally distributed specializations in tech: the talent pool does not concentrate in any single geography, and remote-first work is the norm rather than the exception. If timezone overlap matters for your team, specify the required overlap window in your post. Candidates confirm their availability before applying, so your interview pipeline stays clean.

The highest-converting LLM job posts describe the specific use case (not just "AI features"), name the model stack, explain the production constraints (latency, cost, safety), and specify what success looks like in the first 90 days. For example: "building a multi-tenant RAG system over customer documents — GPT-4o, LangChain, PGVector, FastAPI — we need someone who has designed a chunking and retrieval strategy for noisy, heterogeneous documents before." That specificity attracts senior engineers who recognize the problem, filters out candidates who have only done tutorials, and signals that your team knows what it is actually building.

Free plan: create a company account and applications stay inside RemoteAI — dashboard, messaging, AI matching, no email or external link involved. The $199 option: no account, no dashboard — candidates apply straight to your own email or link instead, and your listing is reviewed and live within one business day.