AI Engineer Interview Guide

The questions an AI engineer loop asks, from how LLMs work to running them in production, with an answer you can say out loud for each.

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Who it’s for

For software engineers moving into AI work, ML engineers whose next job title says “AI engineer”, and new grads with an LLM project on their résumé.

You should be comfortable writing code. The guide assumes no research background.

What it covers

LLM fundamentals
RAG and agents
Evals and production
Your projects

A question from the guide

One of the 21 questions, with its model answer.

LLM fundamentals

How do you choose a model for a new feature?

Model answer

I treat it as a filtering problem with a test set, and I don't start from a leaderboard. First I write down the unit of work: input, expected output, tools, and what counts as success. Then I apply hard gates, things like context size, tool or schema support, data residency, and a latency ceiling. Any model that fails a gate is out, however good its benchmarks look.

The survivors run on a few hundred representative cases under identical prompts and settings, with exact version IDs recorded. I compare task success, p95 latency, and cost per successful task, and I check critical slices separately. A model that's great on average but weak on Spanish inputs might be disqualified. Usually I end up picking the cheapest model that clears every bar, and I rerun the same harness before any version upgrade.

What they’re checking:: Whether you pick models with your own evaluation and explicit constraints, or by reputation.
Likely follow-up:: Two models score within a point of each other on your set. How do you break the tie?

See all 21 questions: AI Engineer Interview Questions and Answers →

What the guide will include

Questions by round
The questions for each part of the loop, grouped the way the interview usually moves.
Answers you can say out loud
Each model answer runs about a minute spoken: the answer first, one example, one tradeoff.
What they’re checking
One line per question on what the interviewer is listening for, plus the likely follow-up.
Mistakes to avoid
The weak answer that costs points, so you can catch it in your own practice runs.
A PDF you can print
Read it on your phone before the call, or print the sections you want to drill.

Planned contents

The guide isn’t out yet, so chapters and their order may still change.

1. Understanding LLMs: tokens, context, sampling and why models make things up
2. Building with model APIs: prompts, structured outputs and tool calls
3. Retrieval and agents: when to use each and how they fail
4. Production: evals, monitoring, cost and latency
5. Talking about your own AI projects
6. A two-week study plan and a three-day version

Free PDF

All 21 questions with model answers

A model answer for every question, what the interviewer is checking, the follow-up and the mistake to avoid.

Questions about the guide

Is the PDF really free?

Yes. Leave your email and we send the PDF the day it is ready. No payment details, and every email has a one-click unsubscribe link.

How is it different from the questions page?

The questions page lists every question with a handful of sample answers. The PDF has a model answer for each question, plus what the interviewer is checking, the likely follow-up and the mistake to avoid.

Is it based on a specific company’s interviews?

No. It is written from the concepts the role or system depends on. We don’t name companies or claim to know what any one company asks.

When will it be ready?

It isn’t out yet. Leave your email and we send it the day it is; nothing else arrives before then.