Modern AI Engineering

Lesson 19.1 · 40 min · video lesson

Cracking the AI Engineering Interview

You understand the material. Can you explain KV caching in 60 seconds, estimate the cost of a chatbot on a whiteboard, and design a RAG system while someone asks "why?" after every box you draw?

In short: AI engineering interviews usually combine general and practical coding, ML and LLM fundamentals, an AI system design round, a deep dive on your past projects, behavioural questions and sometimes a take-home. Interviewers score the evidence you give on a few signals: understanding the problem, depth, judgement, evaluation, production sense and communication. This lesson shows the round structure, model answers from every module, a practical coding task, two worked designs (a RAG support assistant and an agent that takes actions), and a 30-day revision plan.

What you will learn

  • How AI engineering interviews are structured
  • Explaining a concept clearly in 60 seconds
  • Must-know questions from every module
  • The AI system design round
  • A worked system design: a RAG support assistant
  • A 30-day revision plan

In this lesson

  1. How AI engineering interviews are structured
  2. How interviewers score you
  3. Explaining a concept clearly in 60 seconds
  4. Must-know questions from every module
  5. The coding round: what it really asks
  6. The AI system design round
  7. A worked system design: a RAG support assistant
  8. A second walkthrough: an agent that can issue refunds
  9. Worked example, step by step
  10. Practice: try it yourself
  11. Take-homes, work samples and the project deep dive
  12. A 30-day revision plan
  13. Common mistakes and final tips

Key terms in this lesson

System design round
An interview where you design an end-to-end system under stated requirements and explain trade-offs.
Back-of-envelope estimate
A quick rough calculation of traffic, tokens, memory or cost used to guide design decisions.
Recall@k
The fraction of queries for which a correct item appears in the top k retrieved results.
MRR
Mean Reciprocal Rank: the average of 1 / rank of the first correct result across queries.
Golden set
A curated set of inputs with expected outputs used to evaluate a system repeatedly.
Rubric
The list of signals, each with a rating, that an interviewer fills in after a round.
Idempotency key
A unique ID sent with an action so that a retry of the same action is carried out only once.
Abstention
When a system declines to answer, or hands off to a human, because it is not confident.

This lesson is part of the Complete AI Engineer track. See plans and pricing to open the full lesson, its video, labs and quiz, or try the free lesson first (free account needed).

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