Thinking of switching to become an AI engineer? You’ll likely be discouraged by two things first: fragmented tutorials and long articles that are obviously AI-generated—neither systematic nor practical.

I found a more reliable resource on GitHub: the AI Engineering Field Guide. It’s not a “personal experience post” but an open-source guide compiled from 1,765 real job descriptions and actual interview experiences. Data-driven, it directly targets job hunting and getting started.

It clearly breaks down the key information about the AI engineer role: what positions are hiring for, how to match skills, how the interview process works, common question types, and what real cases look like. More importantly, it provides actionable transition learning paths based on different backgrounds like data, backend, and frontend.

GitHub:http://github.com/alexeygrigorev/ai-engineering-field-guide…

Content covers role positioning and skill maps, a complete interview preparation checklist, curated learning resources, and real market data and project cases. It also includes interview processes and experiences from 51 companies including OpenAI, Anthropic, Google, and Meta, along with analysis of 17 real take-home assignments.

If you’re seriously considering transitioning to an AI engineer role, or want to systematically understand what this job actually entails and how to prepare, this guide is worth bookmarking directly.

alexeygrigorev/ai-engineering-field-guide

Source: https://github.com/alexeygrigorev/ai-engineering-field-guide

AI Engineering Field Guide

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.

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 (https://alexeyondata.substack.com/).

The AI Engineer Role

• 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 Preparation

• 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

Learning Paths

• 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

• Project ideas - real project examples that demonstrate AI engineering skills

Job Market Data

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

Awesome AI Engineering

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

A 4-part event series on AI engineering careers, hosted through Maven and AI Shipping Labs (https://aishippinglabs.com/):

1. A Day of an AI Engineer - the practical reality of the role (Maven, AI Shipping Labs (https://aishippinglabs.com/blog/what-is-an-ai-engineer-alexey-grigorev-perspective)) - recording available
2. Defining the AI Engineer Role - what companies actually hire for, based on 2,400+ job descriptions (Maven) - recording available
3. The Interview Process - real hiring trends, technical questions, and live coding challenges (Maven) - recording available
4. Take-Home Assignments - analyzing real assignments and building production-ready solutions (Maven) - recording available

Learn AI Engineering

There’s more to learning AI engineering than watching tutorials:

• AI Engineering Buildcamp: From RAG to Agents (https://maven.com/alexey-grigorev/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 (https://aishippinglabs.com/) - a community of practitioners building AI and with AI, with workshops, courses, case studies, and discussions on what actually works