Tailor Your CV for AI Engineering Roles
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In this workshop we take a CV that's not focused on AI engineering roles and make it more relevant.
We build a pipeline where:
- a renderer turns YAML into Harvard-style CVs
- we take my CV (focused on founder and community work), turn it into YAML, and adjust it for AI engineering roles
- we then adapt it to a particular industry (EdTech)
- and finally, we adjust it for a specific position in that industry
You can reuse this process to adjust your CV and target it to any position.
A few rules keep the CV honest at the end:
- Role titles must be defensible in an interview.
- The headline can position you for the market.
Workshop flow
We work through the CV in this order:
- Turn strategy notes and an existing CV into a focused AI engineering story.
- Move the CV into YAML so later passes edit structured content.
- Render the YAML to a plain HTML CV and print it to PDF.
- Adapt a copy for EdTech, then another copy for one job description.
- Distill the corrections into
CV-PROCESS.mdand test it on another CV.
Links
We build on the job-search workflow from selecting a portfolio project. There we pick a domain, study target companies, and use their roles to decide what to build or feature.
Hosted by
Alexey Grigorev
Chief Agent Officer at AI Shipping Labs
Software engineer and machine learning practitioner with 15+ years of experience building production ML systems. I focus on practical, production-grade ML and AI systems, from early prototypes to reliable systems in production.
I'm the founder of DataTalks.Club, a free community that connects tens of thousands of practitioners worldwide, and the creator of the Zoomcamp series, free, code-first programs that have reached 100,000+ learners globally.
At AI Shipping Labs, I'm building the kind of environment that would have accelerated my own career growth. After years of teaching at scale, I wanted something more focused: a space for action-oriented builders who want to turn AI ideas into real projects. The community gives members the structure, accountability, and peer support to ship practical AI products consistently, even alongside their main jobs.
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