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: 6,964 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.

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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
• Forward Deployed Engineers - growth, responsibilities, and skills from 146 FDE job postings
• Job market trends - month-over-month analysis of role archetypes, skill stacks, and trajectories across 8 scrapes

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

• Portfolio guide - how to choose, build, and present AI engineering portfolio projects

Job Market Data

6,964 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

Webinars and workshops on AI engineering careers, hosted through Maven and AI Shipping Labs:

1. A Day of an AI Engineer - the practical reality of the role (Maven, AI Shipping Labs) - 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
5. Selecting a Portfolio Project - how to choose what to build and scope the first version (AI Shipping Labs, June 29, 2026)

Learn AI Engineering

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