AI Specialist CV Template: Your Projects Are the Proof

A template built for AI and machine learning work, where a candidate is judged on what they built rather than what they studied. It foregrounds projects, models and metrics, and gives experimental results more room than any other template.

The steps

1. Open it, fill in name and title, then state your specialism precisely: vision, NLP, generative models
2. Show three to five projects, each with: problem, data, model, result
3. Give metrics as numbers: precision, recall, latency, data volume, serving cost
4. Add a publications or research section if you have papers or released models
5. List the tools and frameworks you genuinely use, not everything you have heard of

Practical tips

• A negative result is useful information — if an approach failed, mention it briefly; it proves depth of experience
• Name the data constraints you worked under; that is what separates a practitioner from a course graduate
• Link to code or the model where you can; verifiability beats any description

Common mistakes to avoid

• Listing tools and libraries with no projects, so the list reads like a course syllabus
• Omitting the model's production impact: did it cut cost, speed up a decision, improve prediction accuracy?

Frequently asked questions

I am a recent graduate — is this template for me?

Yes, replacing work experience with academic projects and competitions. What matters is that each project carries a measured result rather than a general description.

Do performance metrics matter?

A great deal. A technical recruiter reads metrics before prose, and one honest number beats a whole paragraph.

Links for this guide

• See the AI specialist template
• Use this template in the editor
• Analyse your technical skills gap
• Portfolio builder tool