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5 items tagged with "careers"
A practical guide to project deep dives, Python exercises, unfamiliar tasks, and the evidence hiring managers look for in AI engineering interviews.
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. flowchart LR NOTES["Positioning notes"] --> CLAUDE["Claude Code"] OLD["Existing CV"] --> CLAUDE CLAUDE --> YAML["cv.yml"] YAML --> RENDER["render.py"] RENDER --> HTML["cv.html"] HTML --> PDF["PDF"] JOB["Job description"] --> TAILORED["Company-specific copy"] YAML --> TAILORED TAILORED --> REVIEW["TAILORING.md review notes"] 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.md and 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.
Learn what an AI engineer is in 2026: responsibilities, skills, tools, and real-world use cases based on analysis of 1,000+ AI engineer job descriptions.
How to join AI Engineering Buildcamp: individual enrollment, team discounts, company L&D budgets, student discounts, PPP pricing, and scholarships.
A practical definition of the AI engineer role: what they do, how they differ from ML engineers and data scientists, and what it takes to ship AI-powered features from prototype to production.