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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; some close quickly.

It depends on seniority, location and the company, so we do not quote a typical figure. Many listings on this page show the pay range the employer published: compare those directly, and check whether the range is base salary or includes equity.

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.

Yes, for normal use. Browsing jobs, creating a profile and applying are free, up to 20 applications a day. Credits are optional: they pay for applications beyond that limit or to put an application at the top of the list. No placement fee when you get hired, no share of your earnings, no exclusive contract.

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, describe it specifically.

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. 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 a better fit for 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.