What should an AI engineer resume include?

LLM features you shipped, with users or traffic, how you evaluated quality, latency and cost numbers, the model APIs, retrieval and evaluation tools you used, and a link to a demo or repo.

How do I write an LLM engineer resume without production experience?

Build one project end to end: a RAG app over a real document set with an evaluation set and a cost estimate, deployed so others can try it. Describe it with the same metrics a job would have.

What skills are in demand for AI engineer roles?

Python, model APIs, retrieval and vector databases, evaluation methods, agents and tool calling, and the engineering to deploy and monitor them. Use the job post's exact terms where you have the experience.

Should I list prompt engineering as a skill?

You can, but it's stronger shown inside a bullet about a system you built and measured.

How to write an AI engineer resume (LLM and GenAI roles)

By Mubeen Jawed, founder of ResumeForDev · Updated November 7, 2026

Short answer

An AI engineer resume should show LLM features you shipped to real users and how you measured them: evaluation scores, latency, cost per request, adoption. Write bullets like “Built a RAG support assistant (OpenAI API, pgvector) answering 9k questions a week with 82% resolution on our eval set, at $0.004 per query.” List the model APIs, frameworks, vector stores and evaluation tools you've used, and link a demo or repo. Evaluation and cost numbers are what separate strong AI resumes from demo lists.

Key points

Shipped beats prototyped: show users, traffic or adoption.
Evaluation numbers prove quality. Say how you measured it.
Cost and latency per request are strong, rare metrics.
Name the stack: model APIs, RAG, vector database, orchestration, evals.

AI engineer vs. ML engineer

“AI engineer” usually means building products on top of foundation models: retrieval-augmented generation (RAG), agents, tool use, prompt and evaluation pipelines. “ML engineer” usually means training and serving your own models. Many people do both. Use the title from the posting, and lead with the work that matches it. For model training and MLOps-heavy roles, see the machine learning engineer resume guide.

Before and after

Before:Built a chatbot using LLMs and LangChain.

After:Built a RAG assistant over 12k internal docs (OpenAI API, pgvector, hybrid search) used by 400 support agents; raised answer accuracy on a 300-question eval set from 61% to 84% by adding reranking.

Name the data, the users and how you know it got better.

Metrics for AI and LLM work

Numbers for AI engineer bullets
Metric	Example phrasing
Quality	raised accuracy on a 300-question eval set from 61% to 84%
Hallucination	cut unsupported answers from 9% to 2% with citation checks
Latency	brought p95 response time from 7s to 2.1s with streaming and caching
Cost	cut cost per request 60% by routing simple queries to a smaller model
Adoption	used by 400 agents, answering 9k questions a week
Business	deflected 30% of tier-1 support tickets

Example AI engineer bullets

Built an evaluation harness (pytest plus LLM-as-judge with human spot checks) that runs 500 test cases on every prompt or model change.
Designed a tool-calling agent that drafts refund decisions from order data, with human approval, cutting handling time from 8 to 3 minutes.
Fine-tuned a small open model (LoRA) for ticket classification, matching a hosted model's accuracy at about a tenth of the cost.
Added guardrails for PII redaction and prompt-injection detection before the assistant reached external customers.
Moved embeddings from a hosted vector store to pgvector, removing a vendor and cutting retrieval latency 40%.

AI engineer skills

Models and APIs: OpenAI, Anthropic Claude, Gemini, open models (Llama, Mistral) via vLLM or Ollama
Retrieval: embeddings, vector databases (pgvector, Pinecone, Weaviate), hybrid search, reranking
Frameworks: LangChain, LlamaIndex, or your own orchestration; function calling and structured outputs
Evaluation and ops: eval sets, LLM-as-judge, tracing (Langfuse, LangSmith), prompt versioning
Engineering: Python, TypeScript, FastAPI, Docker, AWS or GCP

On ResumeForDev, set your target role to “AI Engineer”. Without a job post, the keyword check compares your resume against a data and ML vocabulary. Paste a job description and the keyword check uses that post's terms instead; on Pro, the writer also tailors the bullets to it. Before writing, it asks follow-up questions about the numbers above, and it removes any number or skill you never gave it. Your first resume is free.

Frequently asked questions

What should an AI engineer resume include?: LLM features you shipped, with users or traffic, how you evaluated quality, latency and cost numbers, the model APIs, retrieval and evaluation tools you used, and a link to a demo or repo.
How do I write an LLM engineer resume without production experience?: Build one project end to end: a RAG app over a real document set with an evaluation set and a cost estimate, deployed so others can try it. Describe it with the same metrics a job would have.
What skills are in demand for AI engineer roles?: Python, model APIs, retrieval and vector databases, evaluation methods, agents and tool calling, and the engineering to deploy and monitor them. Use the job post's exact terms where you have the experience.
Should I list prompt engineering as a skill?: You can, but it's stronger shown inside a bullet about a system you built and measured.