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Create a free profile and browse the LLM engineer roles on this page. Describe your production LLM experience in detail — the use case, the model and orchestration stack, the evaluation framework you built, and the production results. Companies hiring LLM engineers search profiles directly and respond quickly when they see specific, relevant experience. Apply to roles that match your stack as soon as you see them — LLM positions close fast.

LLM engineers command some of the highest salaries in AI right now. Junior engineers (1–2 years, strong Python and LLM API experience) start at $90K–$120K. Mid-level (2–4 years, has shipped production RAG or agent systems) earn $120K–$160K. Senior LLM engineers (4+ years, owns full LLM product pipeline including evaluation and cost optimization) earn $160K–$220K+. Companies with production systems move fast — strong candidates receive multiple offers simultaneously.

The most valuable skills to highlight: production LLM deployment experience, RAG architecture (chunking, embedding, retrieval quality), evaluation frameworks (RAGAS, custom evals), LangChain or LlamaIndex depth, and cost optimization. Also valuable: function calling, AI agent design (LangGraph, CrewAI), streaming responses, and LLM observability tools like LangSmith. Describe your model stack choices and the reasoning — companies want engineers who make deliberate architectural decisions, not ones who copy tutorials.

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Very competitive — but in a way that favors engineers with real production experience. The market is flooded with candidates who have taken LLM courses or built hobby chatbots. What companies cannot find enough of are engineers who have shipped systems at scale, built proper evaluation frameworks, and solved real production problems like latency, hallucination, and cost control. If you have that experience and describe it specifically, you are in the top tier of candidates.

LLM engineers build the full application stack — APIs, databases, deployment infrastructure, and the LLM integration layer. Prompt engineers specialize in the design, evaluation, and optimization of prompts and retrieval pipelines. In practice these roles overlap significantly. If your primary contribution is building production software systems that happen to use LLMs, you are an LLM engineer. If your primary focus is prompt quality, eval frameworks, and retrieval optimization as a standalone discipline, you are a prompt engineer.

Not always — but it helps significantly. Many companies use a mix of commercial APIs (GPT-4o, Claude) and open-source models (Llama 3, Mistral) depending on use case, cost, and data privacy requirements. Engineers who understand when to use each — and who have hands-on experience with at least one open-source model for fine-tuning or local deployment — are significantly more attractive to companies with cost constraints or sensitive data. Hugging Face experience and familiarity with inference frameworks (vLLM, Ollama) are increasingly requested.

The most effective LLM portfolios include 2–3 documented projects with: the business use case, the model and orchestration stack used, how you measured output quality, a specific challenge you solved (retrieval quality, hallucination, latency, cost), and the outcome. GitHub repositories with clear documentation score well. If you have built something publicly accessible — a demo, an open-source tool, a published evaluation benchmark — include it prominently. Companies evaluate LLM engineers based on judgment and taste, not just technical skill.