AI Engineering Interviews: Free Prep Learning Path

Preparing for AI Engineer interviews means more than leetcode: you need to understand the full funnel (recruiter screen → theory → coding → project deep dive → system design → behavioral), what companies test in each round, and how to talk about LLMs, RAG, agents, evaluation, and production systems.

This path curates two focused resources into a short, ordered prep flow: first a research-backed webinar on how AI Engineer interviews actually work, then a question-bank style video so you can pressure-test your answers on the topics that show up most.

You can watch both inside Curio, take notes, and generate practice quests to rehearse answers before the real thing.

Who This Path Is For

This path is for:

Software engineers moving into AI/ML roles who want to know what to expect end-to-end.
Data scientists or ML engineers rebranding as AI Engineers and preparing for new interview formats (system design, RAG, agents).
Anyone preparing for titles like AI Engineer, ML Engineer, or LLM Engineer and wanting a clear map of theory, coding, project deep dive, and behavioral rounds.

What You Will Learn

By the end of this path you will have covered:

The AI Engineer interview funnel — from recruiter screen to final round, and how questions are collected and used.
Theory interviews — LLMs, RAG, agents, evaluation, and monitoring; why RAG and agents are now standard topics.
Coding interviews — implementation vs algorithm rounds, and how AI coding assistants are changing expectations.
Project deep dives — how hiring managers evaluate seniority and real experience.
AI system design — what to expect and how to structure your answers.
Behavioral interviews — leadership principles and how to prepare.
Practical prep — portfolio projects, networking, referrals, and how to compensate for lack of production experience.
Real interview questions — LLMs, transformers, data ingestion, embeddings, hallucinations, latency, cost, retrieval accuracy, concurrency, and real-time vs batch.

AI Engineering Interview Prep Roadmap

Map the funnel — How AI Engineer interviews work (theory, coding, project deep dive, system design, behavioral).
Pressure-test with real questions — LLMs, RAG, design problems, concurrency, embeddings, latency, cost, and retrieval.

Estimated learning time: ~2–3 hours (watch in order; use timestamps to jump to weak spots).

Skill level: Intermediate (assumes some exposure to LLMs or software engineering).

After each video: write down 2–3 answers you’d give to the most common question types. Use Curio notes and tag AI interviews, RAG, or system design in your knowledge graph.

Step-by-Step AI Engineering Interview Prep Path

Step 1 — How the AI Engineer Interview Funnel Works

A research-backed webinar (Alexey Grigorev, AI Engineering Field Guide) walks through how companies actually run AI Engineer interviews: data sources (job descriptions + interview reports), methodology, and the typical funnel. Covers theory interviews (LLM, RAG, agents, evaluation, monitoring), coding rounds (implementation vs algorithm, with an example), project deep dives (how hiring managers evaluate candidates), AI system design, and behavioral interviews. Includes Q&A on preparation, portfolio strategy, production experience, referrals, and whether RAG is a prerequisite for agentic systems.

Watch

Key takeaways

Funnel: Recruiter screen → theory → coding (implementation and/or algorithm) → project deep dive → system design → behavioral. Know which round tests what.
Theory interviews often cover: how LLMs work, RAG, agents, evaluation, and monitoring. RAG and agents are now common; prepare concrete explanations and tradeoffs.
Coding: Companies use both implementation (build a small feature) and algorithm rounds. AI coding assistants are in the picture—understand what’s allowed and how to show your reasoning.
Project deep dives are where seniority and real experience are evaluated. Be ready to go deep on one or two projects: problem, your role, tradeoffs, failures, and what you’d do differently.
AI system design is its own round: designing workflows, services, and data flows for AI-powered products. Practice structuring your answer (requirements, high-level design, key components, scaling, failure modes).
Behavioral and leadership principles matter. Align your stories with the company’s principles; prepare STAR-style examples.
Portfolio and prep: Use take-homes and side projects to simulate real work. Choose project domains that match target roles. Network and seek referrals; they often unlock the first conversation.
Production experience: If you lack it, emphasize observability, logging, evaluation, and quality—and use portfolio projects to show you think about production concerns.

Suggested segments to watch

00:00 — Introduction and webinar series overview
00:20 — Recap: What is an AI Engineer?
01:03 — AI Engineering Field Guide repository
04:15 — Data sources (job descriptions + interview reports)
07:28 — Research methodology and dataset overview
12:15 — Typical AI Engineer interview funnel
15:56 — How interview questions were collected and filtered
21:43 — Theory: LLM, RAG, agents, evaluation, monitoring
30:18 — Q&A: How to prepare for AI Engineer interviews
36:47 — Q&A: Future of AI Engineering vs Data Science and ML
43:13 — Coding: implementation vs algorithm rounds
48:39 — Example algorithm problem (run-length encoding)
52:50 — Project deep dive interviews (how hiring managers evaluate)
01:00:43 — AI system design interviews
01:08:23 — Behavioral interviews and leadership principles
01:13:49 — Preview: take-home assignments
01:15:19 — Q&A: Using take-homes to prepare
01:19:38 — Q&A: Portfolio strategy and project domains
01:23:08 — Q&A: Overcoming lack of production experience
01:27:46 — Q&A: Should juniors apply to senior AI roles?
01:28:23 — Observability, logging, and evaluation skills
01:32:57 — Q&A: Production experience expectations
01:38:46 — Q&A: Networking and referrals
01:42:02 — Is RAG a prerequisite for agentic systems?
01:44:05 — Coding interviews and AI coding assistants
01:45:10 — Closing remarks

Useful links from the session: AI Engineering Field Guide, Awesome AI Engineering.

Learn With Curio

Capture the funnel stages and one takeaway per round (theory, coding, deep dive, system design, behavioral). Tag AI interviews, funnel, and system design in your knowledge graph. After watching, generate a practice quest to rehearse a 2-minute answer to “How would you design a RAG system for X?”

Step 2 — AI Engineer Interview Questions to Practice

A question-bank style resource covering the kinds of questions that show up in AI Engineer interviews: fundamentals (how LLMs and transformers work), system design (e.g. removing dead links at scale), concurrency and async (race conditions, asyncio), data ingestion (structured vs unstructured vs interaction data), and GenAI-specific topics (embeddings, hallucinations, latency, cost, retrieval accuracy). Use this video to pressure-test your answers and fill gaps.

Watch

Key takeaways (question areas to prepare)

Fundamentals: How do LLMs work? How do transformers work?
System design: How would you design an AI workflow to remove all dead links for hundreds of client websites? (Think: scale, idempotency, observability, failure handling.)
Concurrency and async: How do you handle race conditions? How does concurrency work in Python? Problems with asyncio and asynchronous programming.
Real-time vs batch: When is real-time vs batch preferred for data updates? How would you choose?
Data ingestion: How would you ingest and process structured data (e.g. SKUs), unstructured (reviews, FAQs), and user interaction data (logs)? How do you ensure the quality of data that an LLM interacts with?
Embeddings: How would you generate and store embeddings for products and queries in a chatbot? Best practices for generating and storing embeddings.
GenAI reliability: How to prevent LLM hallucinations; how to handle exceptions in LLM/GenAI applications.
Performance and cost: How to reduce latency in GenAI applications (e.g. chatbots); how to reduce costs; how to improve retrieval accuracy.

Learn With Curio

For each question area, write a 1–2 paragraph “answer sketch” in Curio. Tag RAG, embeddings, latency, and evaluation in your knowledge graph. Generate a practice quest that asks you to explain one design (e.g. “Design a RAG pipeline for product search”) in under 5 minutes.

Turn Watching Into Real Prep

Watching interview prep once is not enough—you need to rehearse answers and fill gaps.

With Curio you can:

take notes while watching and turn them into answer outlines
generate practice quests to simulate “explain X in 2 minutes”
tag concepts (RAG, evals, system design) in your knowledge graph so you can review before interviews

That turns these two videos into a reusable prep system.

Continue Your Learning

Once you’ve finished this path, go deeper on the technical side:

Strong fundamentals in LLMs, RAG, and evaluation will make your interview answers sharper and more concrete.

FAQ

How long does it take to prepare for AI Engineer interviews?

This path is 2–3 hours of focused watching. Real prep also means practicing answers out loud, doing mock system design, and building or refining a portfolio project. Plan for at least a few weeks if you’re new to RAG, agents, or system design.

Do I need production experience to get an AI Engineer role?

Not always. Many teams care about thinking in production terms: observability, evaluation, data quality, and failure modes. Use portfolio projects and take-homes to show you consider these. The first video’s Q&A covers how to address lack of production experience.

Are RAG and agents really common in interviews?

Yes. Theory rounds often include RAG (retrieval, chunking, embeddings, retrieval accuracy) and agents (orchestration, tools, failure handling). Prepare clear, concise explanations and be ready to design a simple RAG or agent workflow on a whiteboard or in a system design round.