An LLM Engineer designs and operates applications built around large language models. The role focuses on language-model behaviour, retrieval, tool use and evaluation alongside the software engineering needed to run those capabilities in production.
What does an LLM Engineer do?
The scope depends on the product and team. Typical responsibilities include:
- Design prompts, context handling, structured outputs and behaviour when answers are uncertain.
- Build retrieval and grounding using approved documents and suitable search techniques.
- Compare model, prompt and retrieval changes against task-specific evaluation sets.
- Implement tracing, permission checks, timeouts and validation of model-generated tool arguments.
Key LLM Engineer skills to assess
Python or TypeScript; APIs; retrieval-augmented generation (RAG); embeddings; search; structured outputs; evaluation design; observability. A vector database or orchestration library is an implementation choice, not proof of expertise by itself.
Separate day-one requirements from skills a candidate can learn. Ask which decisions they owned and how they checked results. A technology listed on a CV does not establish hands-on depth.
A practical example of the role
A support assistant needs current product documentation. The engineer measures retrieval coverage separately from answer quality, tests misleading instructions inside retrieved content and provides an escalation route for unsupported questions.
How this role differs from related positions
LLM engineering centres on large language models. NLP also includes classification, extraction and ranking with smaller models. Generative AI engineering can extend beyond text to image, audio and multimodal systems.
How to hire an LLM Engineer
Define the work before starting the search: your existing system, delivery stage, expected ownership and who will review technical decisions. Align the seniority, working format and communication requirements with those responsibilities.
Use a relevant technical discussion or work sample to explore these questions:
- How do you separate retrieval failures from generation failures?
- When would you use RAG, fine-tuning or ordinary search?
- Where are tool permissions enforced independently of the prompt?
Look for a clear explanation of the candidate’s own contribution, trade-offs and lessons from unsuccessful approaches. Match the assessment to the actual role rather than relying only on tool names or a generic quiz.
Frequently asked questions
Is prompt writing enough for this role?
No. Production work normally also requires integration, evaluation, data handling and operational judgement.
What should an LLM Engineer job description include?
Include business context, core tasks, existing stack, expected seniority, reporting line and success criteria. Add the salary range, location or time-zone requirements and working format where available. Distinguish essential requirements from preferences.
Find the right LLM Engineer for your team
Web Recruiters helps companies search for technical specialists across Europe, Israel, the USA, Canada and Latin America. A dedicated manager coordinates the search and feedback. Initial screening can cover English, salary expectations, availability, working format and agreed client criteria. The client normally makes the final technical assessment; additional technical interviews can be discussed separately.
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Related technology roles
Further technical reading: Machine learning fundamentals — Google.