Data-driven field guide to AI engineering roles, skills, and interviews.
Everything here is based on real data: 2,445 actual job descriptions, real interview experiences, and real stories from practitioners. This is not AI-generated filler dumped into a repo - every insight comes from analyzing actual data and synthesizing patterns from it.
My vision for this repo is to become the go-to resource for AI engineering. Like data-science-interviews but broader:
- role analysis
- job market data
- interview questions
- learning paths
- and more
It's a work in progress, and I'm actively adding more content. Your input is very welcome - feedback and contributions help shape what goes in here.
Star this repo to keep an eye on updates. To get notified about new content, subscribe to my newsletter: Alexey on Data.
- My vision of the role - how I see AI engineering, comparison with DS/ML/DE roles, CRISP-DM for AI
- Skills analysis - top skills, job types, cloud platforms, frameworks
- Responsibilities - patterns extracted from 5,694+ job responsibilities
- Use cases - 4,525 real use cases showing what companies build with AI
- Reality vs. job postings - what candidates experience vs. what's advertised
- Interview process - common patterns, step counts, time estimates, AI use in hiring, key takeaways
- Interview questions - consolidated from 100+ sources
- Theory - LLMs, RAG, agents, ML fundamentals, company-specific questions
- Coding - coding round formats, DSA problems, ML implementation exercises
- Project deep dive - presentation rounds, follow-up probes, what interviewers evaluate
- AI system design - system design for AI applications
- Behavioral - values, leadership, problem-solving
- Home assignments - take-home assignments and paid work trials from 100+ GitHub repos
- Skills that get you hired - baseline expectations, differentiators, and portfolio strategy
- After the interview - handling offers, rejections, and salary negotiation
- Interview trends - realistic assessments, AI cheating, AI-proctored rounds
- Company-by-company data - individual interview process descriptions for 51 companies, linked to source job postings
- General learning path - what to learn and in what order
- From Data Engineer - smoothest transition, 3-4 months
- From Data Scientist - evaluation is your superpower, add engineering
- From ML Engineer - easiest transition, replace model call with API call
- From Backend Engineer - 2-3 months, add AI on top of engineering
- From Frontend Engineer - backend first, then AI, unique full-stack advantage
- Project ideas - real project examples that demonstrate AI engineering skills
2,445 job descriptions scraped from builtin.com covering LA, NY, London, Amsterdam, Berlin, and India.
- Structured job descriptions - YAML files grouped by scrape date
- Raw extracted postings - original extracted data grouped by scrape date
Curated collection of resources we compiled while researching content for this field guide:
- Practitioner interview stories
- AI system design guides
- Company engineering blogs
- Books and courses
- Case study collections
See awesome.md for the list.
- Salary analysis and compensation data
- Community-contributed interview experiences
A 4-part event series on AI engineering careers, hosted through Maven and AI Shipping Labs:
- A Day of an AI Engineer - the practical reality of the role (Maven, AI Shipping Labs) - recording available
- Defining the AI Engineer Role - what companies actually hire for, based on 2,400+ job descriptions (Maven) - recording available
- The Interview Process - real hiring trends, technical questions, and live coding challenges (Maven) - March 3, 2026
- Take-Home Assignments - analyzing real assignments and building production-ready solutions (Maven) - March 9, 2026
Have questions? Submit them here - all questions will be covered during the events or afterwards.
If you want to learn the core skills needed for being an AI engineer, check out my course AI Engineering Buildcamp: From RAG to Agents - a 9-week intensive on building production-ready AI applications.
