Data-driven field guide to AI engineering roles, skills, and interviews. AI Engineering Field Guide is the go-to resource for AI engineering.
Everything here is based on real data: 4,894 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.
Star this repo to keep an eye on updates. To get notified about new content, subscribe to my newsletter: AI Shipping Blog.
- 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
- Forward Deployed Engineers - growth, responsibilities, and skills from 146 FDE job postings
- 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
- Portfolio guide - how to choose, build, and present AI engineering portfolio projects
4,894 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
- Job market trends - month-over-month analysis of role archetypes, skill stacks, and trajectories across 6 scrapes
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.
Webinars and workshops 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) - recording available
- Take-Home Assignments - analyzing real assignments and building production-ready solutions (Maven) - recording available
- Selecting a Portfolio Project - how to choose what to build and scope the first version (AI Shipping Labs, June 29, 2026)
There's more to learning AI engineering than watching tutorials:
- AI Engineering Buildcamp: From RAG to Agents - my 9-week intensive course on building production-ready AI applications, covering RAG, agents, testing, evaluation, and monitoring
- AI Shipping Labs - a community of practitioners building AI and with AI, with workshops, courses, case studies, and discussions on what actually works