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8 items tagged with "ai-engineering"
A practical guide to project deep dives, Python exercises, unfamiliar tasks, and the evidence hiring managers look for in AI engineering interviews.
We will run two mock hiring-manager interviews for volunteer participants, focused on AI engineering roles. The format is useful for most software engineering interviews too. Each interview will roughly follow this structure: - 5 minutes: introduction - project deep dive - about 20 minutes: coding task - 5 minutes: candidate questions After the interviews, we will discuss what happened, what worked well, and how to prepare for this interview style. Volunteer preparation: - Prepare a 2-3 minute self-introduction. - Select 2-3 past projects and review them before the session. - Be ready to explain the problem each project solved, your role and contribution, key technical and product decisions, technologies used, tradeoffs, and why you made those choices. - For more senior roles, be ready to connect the project to business metrics and explain how success was measured. We will invite two volunteers to participate live.
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.
See how CRISP-DM still guides AI engineers in 2026, translating each phase into practical workflows for LLM apps, RAG pipelines, and production AI systems.
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.
A visual learning path for becoming an AI engineer in 2026, based on analysis of 1,000+ job descriptions.