In short
AI hiring managers pattern-match in about ten seconds for evidence you have actually built with modern AI tooling. Lead with a structured skills section rather than an objective, surface the AI component of every role even when it was secondary, and give personal projects their own section.
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.
Frequently asked
What do AI hiring managers look for in the first ten seconds?
A fast pattern-match for proof you have built things with modern AI tooling rather than read about it. They are scanning for named platforms, frameworks and shipped systems, not for enthusiasm or general software-engineering breadth.
How should the skills section be structured?
Lead with skills rather than an objective statement, and group them by category: LLM platforms, agent frameworks, and supporting infrastructure. Naming specific models and versions carries far more signal than a flat list of technologies.
How do I present work experience that was not primarily AI?
Rewrite each role so the AI or agent component is explicit, even when it was a small part of a larger project. The guide walks through before-and-after transformations that surface the agent work rather than burying it inside a general engineering description.
Do personal projects matter on an AI resume?
They do disproportionate work if you are transitioning from traditional software engineering. Give them a dedicated section and, for each, state the problem, what you built and what the outcome was.
What should I cut?
Pure traditional software engineering with no AI angle, if space is tight. Older framework projects and long lists of programming languages take room away from the evidence hiring managers are actually scanning for.