AI engineer interview preparation, round by round
AI engineer interviews focus on LLM basics, RAG, agents and evaluation, then GenAI system design. Some companies also keep an ML basics round and a DSA round.
27 resources · 20 free · plus 23 for every role · Links checked 15 October 2026 · How we pick
Change role
- Software engineer 54 resources
- Backend developer 45 resources
- Frontend developer 37 resources
- Full stack developer 43 resources
- Mobile developer 19 resources
- DevOps and SRE 24 resources
- Data engineer 23 resources
- Data analyst 21 resources
- Data scientist 24 resources
- ML engineer 31 resources
- AI engineer 27 resources
- QA and SDET 12 resources
Start here
Three resources, in this order. Everything below is for going deeper on one round.
- Awesome Generative AI Guide: AI Interview Hub
GitHub repo · Free
AI engineer interviews differ a lot between companies. Its round breakdowns and prep plans give you a structure to follow.
- AI Engineering Interview Questions (Amit Shekhar, Outcome School)
GitHub repo · Free
Use it as a checklist of LLM, RAG and agent topics. Tick a topic off only when you can explain it without notes.
- What We Learned from a Year of Building with LLMs (applied-llms.org)
Blog · Free
Read it before a GenAI system design round. It gives you real trade-offs on prompting, RAG and evaluation to discuss.
Nothing matches these filters. Use Show all to see every resource again.
LLMs and GenAI 16 resources
How LLMs work, plus RAG, agents, evaluation and building on model APIs
Awesome Generative AI Guide: AI Interview Hub
GitHub repo · aishwaryanr/awesome-generative-ai-guide
Role-based GenAI interview folders with round breakdowns, answered question banks and prep plans, plus ten worked GenAI system design case studies with runnable code.
Best for structuring GenAI or AI engineer prep around a ready-made plan
Also in ML system design, Resume, companies and offers
AI Engineering Interview Questions (Amit Shekhar, Outcome School)
GitHub repo · amitshekhariitbhu/ai-engineering-interview-questions
A long checklist of AI engineer questions on LLM basics, prompting, RAG, agents, fine-tuning, vector databases, LLMOps and evals, about half linked to free answers.
Best for Indian candidates aiming at GenAI and LLM engineer roles who want a topic checklist
Also in ML system design
What We Learned from a Year of Building with LLMs (applied-llms.org)
Blog · applied-llms.org
A long practitioner essay on what works when shipping LLM products, from prompting, RAG and evals up to team and strategy decisions.
Best for AI engineer design and 'how would you ship this?' discussions
Also in ML system design
The Illustrated Transformer (Jay Alammar)
Blog · jalammar.github.io
A diagram-heavy walkthrough of self-attention, multi-head attention, positional encoding and the encoder-decoder flow, for explaining transformers clearly.
Best for preparing a clean whiteboard explanation of transformers and attention
Also in ML theory and statistics
LLM Course (Maxime Labonne)
GitHub repo · mlabonne/llm-course
A roadmap split into fundamentals, LLM scientist and LLM engineer tracks, pairing curated reading with Colab notebooks on fine-tuning, quantisation, RAG and deployment.
Best for filling gaps across the LLM stack before AI engineer interviews
AIMLInterviews (formerly Machine-Learning-Interviews)
GitHub repo · alirezadir/AIMLInterviews
A big-tech ML interview guide arranged by round: coding, ML coding, ML breadth including GenAI and agents, ML system design and behavioral, from the author's own loops.
Best for planning FAANG-style ML engineer or applied scientist prep one round at a time
Also in ML system design, ML theory and statistics, Coding and DSA
TorchCode
GitHub repo · duoan/TorchCode
Forty-one auto-graded notebook problems where you write PyTorch pieces such as softmax, LayerNorm, multi-head attention and GPT-2 blocks from scratch.
Best for practising the PyTorch implementation questions asked by AI labs and LLM teams
Also in ML theory and statistics
Building Effective Agents (Anthropic)
Official guide · anthropic.com
Anthropic's guide on when a fixed LLM workflow beats an autonomous agent, with patterns such as prompt chaining, routing, orchestrator-workers and evaluator-optimiser.
Best for agent design questions in AI engineer interviews
Also in ML system design
Evidently AI ML & LLM System Design Case Studies
Website · evidentlyai.com
An open index of about 800 company write-ups on how real teams built recommenders, fraud and search systems and LLM, RAG and agent products, filterable by industry.
Best for collecting real design patterns to cite in ML and GenAI system design rounds
Also in ML system design
Machine Learning Q and AI (Sebastian Raschka)
Book · sebastianraschka.com
Thirty short chapters, each built around one question on deep learning, vision, NLP and LLMs, deployment or evaluation, that work as practice for 'explain X' rounds.
Best for mid-level ML and AI candidates who want depth on conceptual follow-up questions
Also in ML theory and statistics
Build a Large Language Model (From Scratch): code repo (Sebastian Raschka)
GitHub repo · rasbt/LLMs-from-scratch
Step-by-step PyTorch notebooks that build, pretrain and fine-tune a GPT-style model, plus bonus material on newer architectures, with the matching book sold separately.
Best for explaining and coding attention, pretraining and fine-tuning in depth
Also in ML theory and statistics
AI Engineering (Chip Huyen) and companion repo
Book · chiphuyen/aie-book
A book on building products on foundation models, from evaluation, prompting, RAG and agents to fine-tuning and inference cost, with a free repo of summaries and notes.
Best for a complete mental model for AI engineer interviews on RAG, evals and agents
Also in ML system design
Lil'Log (Lilian Weng)
Blog · lilianweng.github.io
Long, heavily referenced deep dives on LLM agents, prompting, hallucination, RLHF and reward hacking, diffusion models and scaling laws.
Best for senior or research-leaning AI roles that probe the theory behind LLMs
Also in ML theory and statistics
Hugging Face LLM Course
Course · huggingface.co
A free hands-on course on transformers, tokenisers, datasets, fine-tuning and newer LLM techniques, using the Hugging Face libraries interviewers expect you to know.
Best for practical transformer and fine-tuning experience to talk about in interviews
Krish Naik (YouTube)
YouTube channel · youtube.com
An Indian educator's channel with data science and ML interview-question playlists, recorded mock interviews, end-to-end projects and GenAI tutorials in English and Hindi.
Best for Indian freshers and career switchers moving into data science, ML or GenAI
Also in ML theory and statistics
Neural Networks: Zero to Hero (Andrej Karpathy)
Video · karpathy.ai
Eight long video lectures that build backprop, character-level language models, a GPT and its tokenizer in code, starting from an empty file.
Best for explaining and coding transformers and backprop from first principles
Also in ML theory and statistics
ML system design 2 resources
Designing ML systems end to end: data, training, serving and monitoring
Designing Machine Learning Systems (Chip Huyen)
Book · chiphuyen/dmls-book
The best-known book on production ML; the linked companion repo has free chapter summaries, an ML refresher and MLOps tool notes, while the book itself is paid.
Best for mid and senior ML engineers preparing for open-ended ML system design rounds
Made With ML (Goku Mohandas)
Course · madewithml.com
A free course that takes one ML application through data prep, training, tuning, testing, serving, CI/CD and monitoring, with production-style code.
Best for talking about MLOps practice and production trade-offs with real substance
ML theory and statistics 2 resources
Statistics, probability, A/B tests, ML algorithms and how to judge a model
Introduction to Machine Learning Interviews (Chip Huyen)
Book · huyenchip.com
A free online book that explains ML roles, hiring loops and offers, then lists 200+ maths, CS and ML questions by difficulty, about a tenth with written answers.
Best for a first read for any ML or data science candidate who wants to know what each round tests
Also in Resume, companies and offers
Deep-ML
Practice site · deep-ml.com
A LeetCode-style judge where you implement ML maths and models in Python, from linear algebra and optimisers to attention and a small GPT, against test cases.
Best for the ML coding round, where you write algorithms from scratch in NumPy or PyTorch
Coding and DSA 6 resources
Solving problems with data structures and algorithms, and explaining your approach
Amazon - How We Hire (role-specific interview prep)
Official guide · amazon.jobs
Amazon's hiring hub, explaining each stage from application to the final loop, with role-specific prep pages for SDE, front-end, BI engineer and scientist roles.
Best for finding out what Amazon tests for your role and level, and how its loop works
Also in Resume, companies and offers
Grind 75
Website · techinterviewhandbook.org
A study-plan generator that turns a ranked list of high-frequency LeetCode problems into a week-by-week schedule sized to the hours you have.
Best for candidates with a fixed number of weeks who want the highest-value problem list
Canva - "Yes, you can use AI in our interviews"
Official guide · canva.dev
A Canva engineering post on why it now expects backend, frontend and ML candidates to use AI coding tools in interviews, and what interviewers assess instead.
Best for understanding AI-assisted coding rounds and how to show good judgement with an AI tool
Also in Resume, companies and offers
NeetCode Roadmap (NeetCode 150)
Website · neetcode.io
A visual topic map of 150 core LeetCode problems, each linked to a free video walkthrough on the NeetCode YouTube channel.
Best for learning problem patterns topic by topic, with a video solution for every problem
LeetCode
Practice site · leetcode.com
The default site for interview-style coding practice: thousands of problems, free study plans such as Top Interview 150 and SQL 50, and weekly contests.
Best for daily practice and timed contests (Premium mainly adds company-tagged lists)
interviewing.io - Mock interview replays
Practice site · interviewing.io
A free library of recorded, anonymised mock interviews run by big-tech engineers, filterable by company, round and level, with paid live mocks available.
Best for watching real interviewer feedback on coding, design and behavioral rounds before your own mocks
Resume, companies and offers 1 resource
Getting shortlisted, researching companies, and comparing and negotiating offers
interviewing.io - Senior engineer's guide to FAANG interviews
Website · interviewing.io
A free long-read built from talks with FAANG interviewers that compares how Meta, Amazon, Apple, Netflix, Google and Microsoft run their loops and team matching.
Best for experienced engineers planning a FAANG or big-tech job search end to end
Same for every role
Behavioral rounds, resumes, pay and company guides do not change with the job. These 23 live on the interview resources hub.
Behavioral and HR
- GeeksforGeeks - HR interview questions
- Microsoft Careers - Interview tips
- MIT CAPD - The STAR method for behavioral interviews
- Tech Interview Handbook - Behavioral interviews
- reverse-interview
- Amazon Leadership Principles
- Google Careers - Interview tips
- Jeff H Sipe - Practice Interviews (YouTube)
- A Life Engineered (Steve Huynh, YouTube)
Practice and communities
Resume, companies and offers
- Tech Interview Handbook - Software engineer resume guide
- Reactive Resume
- Jake's Resume (LaTeX template on Overleaf)
- Hiring Without Whiteboards
- r/developersIndia Community Wiki
- AmbitionBox
- Anthropic - Candidate AI guidance
- Haseeb Qureshi - Ten rules for negotiating a job offer
- Thoughtworks India - AI policy for recruitment
- Levels.fyi - Software engineer pay in India
- r/EngineeringResumes wiki
From the team that keeps this list
Helpwing is a desktop app for interviews where AI help is allowed: it hears the question and drafts an answer from your resume. It is not for online assessments or tests.