How to Write a Resume for AI and Agentic Roles

I've reviewed north of 500 resumes for AI and agentic roles over the past 18 months. The ones that get callbacks share a handful of specific characteristics. The ones that get filtered out, even from strong engineers, make predictable, fixable mistakes. Let me show you both.

What Hiring Managers Are Scanning For (First 10 Seconds)

AI hiring managers are doing a fast pattern-match for signals that you've actually built things with modern AI tooling, not that you've read about it. The first pass is: do I see the words LLM, agent, RAG, eval, or specific model names anywhere on this page? If not, the resume often gets deprioritized before they've read a sentence.

This sounds superficial, and it is. But it means your formatting and keyword choices matter enormously. Don't bury your AI work at the bottom of a long list of traditional SWE accomplishments.

The Skills Section

Lead with a skills section, not an objective statement. Structure it like this:

• LLM Platforms: OpenAI (GPT-4o, o3), Anthropic (Claude 3.5 Sonnet), Google Gemini 2.0, Cohere Command R+
• Agent Frameworks: LangGraph, AutoGen, CrewAI
• RAG Stack: pgvector, Pinecone, Weaviate, Cohere Embed, OpenAI Embeddings
• Observability: Langfuse, LangSmith, Braintrust
• Languages: Python (primary), TypeScript

Be specific. "Experience with AI tools" is meaningless. "Built multi-agent research pipeline with LangGraph, tracing via Langfuse, evaluated with custom LLM-as-judge suite" is compelling.

Rewriting Your Work Experience

For each role, you need to surface the AI/agent work specifically, even if it was a side component of a larger project. Some examples of transformations:

Before: "Built customer support automation tool that reduced ticket volume by 30%."

After: "Designed RAG-based customer support agent using GPT-4o and Pinecone over 50K support documents; built evaluation suite measuring 87% response accuracy; reduced ticket volume by 30% over 8 weeks."

The second version tells me: you know RAG, you understand evals, you can measure outcomes. The first tells me nothing AI-specific.

Personal Projects: Your Secret Weapon

If you're transitioning into AI from traditional SWE, your personal projects are doing disproportionate work on your resume. Give them a dedicated section. For each project include:

• What problem it solves (one sentence)
• The specific AI stack you used
• What you evaluated it on and what the results were
• A link to the GitHub repo and/or a live demo

Hiring managers at AI-native companies will often check your GitHub before your LinkedIn. Make sure the repos have clear READMEs with setup instructions and documentation of design decisions.

What to Leave Off

Cut anything that's pure traditional SWE without an AI angle if you're tight on space. A 2019 Rails project, your experience with JQuery, a list of 15 programming languages, none of this helps you and it dilutes your AI signal.

Also: don't claim fluency in models or frameworks you've only read about. Interviewers will ask specific questions, and getting caught overstating your experience is worse than being honest about your learning path.

Tailoring for Specific Roles

Before applying to any role, spend five minutes extracting the specific technical requirements from the job description and making sure your resume mirrors that language. If they list "experience with function calling and tool use," make sure those exact words appear in your resume if they're true.

When you're applying to roles on AgenticCareers.co, the job descriptions tend to be technically specific enough to make this tailoring straightforward, use them as your keyword list.

More interview prep: AI agent engineer resume examples and templates, the agentic AI interview questions you'll face next, and the software-engineer-to-AI-engineer career switch guide.