AI Engineer Interview Prep Roadmap

Interview preparation roadmap · Technology

Building products on large language models: retrieval, evaluation, ML basics and the back-end around them.

LLMsMLBackend

0 / 20 complete · 12 core · 8 other · 109 subtopics

Start here

01Interview foundations
02Coding
03ML foundations
04AI engineering
05Practice
06Company & final prep

CoreCommonly assessedGood to knowRole-dependentAdvancedFor deeper interviewsOptionalNot needed by everyoneCompletedYour progressYou are hereNext up

01Interview foundations
What every interviewer sees first.
1. 01.1Resume
  CoreBeginner3–5 h
  A one-page resume you can defend line by line.
  What this covers
  - One-page format — Reverse-chronological, clean fonts, no photos or tables that confuse parsers.
  - Project bullets — Action + what you built + a number (users, speed, accuracy).
  - Skills section — Only skills you can answer questions on for two minutes.
  - ATS readability — Export a text-based PDF and check it with a parser.
  - Working links — GitHub, LinkedIn and a live project link that open.
  - Tailoring — Reorder bullets to match the job description.
  What to practise
  - Run your PDF through a parser test
  - Rewrite each bullet as action + result
  - Remove any skill you can't explain for 2 minutes
  Common questions
  - Walk me through your resume.
  - Which project here are you most proud of, and why?
  - Why is this skill on your resume?
  Free resources
  - OpenResume — Free ATS-friendly resume builder and parser test (Free)
  - Jake's Resume (Overleaf) — The LaTeX resume template most SDE freshers use (Free)
  - Resume Worded — Resume and LinkedIn scoring (Free plan)
  - Jobscan — Match your resume against a job description (Free plan)
  Free courses
  - Professional Resume Writing — Alison (Free course)
  - Resume, Networking, and Interview Skills — Fullbridge (Free to audit)
2. 01.2Tell me about yourself
  CoreBeginner1–2 h
  A 60–90 second answer: present, past, why this role.
  What this covers
  - Present — Who you are now: degree, year, focus.
  - Past — One or two proof points: a project, internship or result.
  - Future — Why this role and this company next.
  - Length — 60–90 seconds; stop before you list everything.
  - Delivery — Calm pace and eye contact, not a memorised script.
  What to practise
  - Write it, then say it out loud 5 times
  - Record yourself and cut anything over 90 seconds
  Common questions
  - Tell me about yourself.
  - Why should we hire you?
  - Where do you see yourself in a few years?
  Free resources
  - Google Interview Warmup — Free spoken mock interview that prints your transcript (Free)
  - BBC Learning English — Structured free English lessons and podcasts (Free)
  Free courses
  - English@Work: Basic Job Interview Skills — The Hong Kong Polytechnic University (Free to audit)
  - Job Interview Preparation For Tech Professionals — University of Cape Town (Free to audit)
3. 01.3Behavioral & STAR
  CoreBeginner3–4 h
  Stories about teamwork, conflict and failure, told with STAR.
  What this covers
  - STAR structure — Situation, task, action, result. Spend most of the time on action.
  - Story bank — 6–8 real stories you can reuse across questions.
  - Teamwork & conflict — Disagreeing respectfully and what you changed.
  - Failure & weakness — A real one, what you learned, what you do now.
  - Ownership — Times you took initiative without being asked.
  - Common HR questions — Why us, why you, strengths, relocation.
  What to practise
  - Write 6 stories in STAR form
  - Map each story to 2–3 common questions
  - Practise one story with a friend
  Common questions
  - Tell me about a time you failed.
  - Describe a conflict in a team and what you did.
  - What is your biggest weakness?
  Free resources
  - Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
  - Google Interview Warmup — Free spoken mock interview that prints your transcript (Free)
  - Pramp / Exponent peer mocks — Free peer-to-peer mock interviews (Free plan)
  Free courses
  - Master Behavioral Interviews (for Software Engineers) — freeCodeCamp (Free video)
  - Behavioral Interview Practice for Computer Science Students — CodeSignal (Free to audit)
  - Soft Skills For Tech Professionals — University of Cape Town (Free to audit)
02Coding
Most loops include coding.
1. 02.1Programming fundamentals
  CoreBeginner6–10 h
  One language you know well: syntax, memory, complexity.
  What this covers
  - One language deeply — C++, Java or Python: syntax, standard library, quirks.
  - Time & space complexity — Big-O of loops, recursion and common operations.
  - Recursion — Base cases, the call stack, when it overflows.
  - Memory basics — Stack vs heap, references vs values, garbage collection.
  - Strings & arrays — Immutability, copying and slicing costs in your language.
  - Debugging — Reading errors, dry runs, using a debugger.
  What to practise
  - Solve 10 easy problems without an IDE's help
  - Explain the complexity of each solution aloud
  Common questions
  - What is the time complexity of your solution?
  - What happens in memory when you call a function?
  - Difference between an array and a linked list?
  Free resources
  - Harvard CS50x — The best first CS course there is (Free)
  - HackerRank — Where many campus tests actually run (Free)
  - GeeksforGeeks — Company-wise questions and theory (Free plan)
  Free courses
  - CS50's Introduction to Programming with Python (CS50P) 2022 — Harvard University (Free video)
  - MIT 6.100L Introduction to CS and Programming using Python, Fall 2022 — Massachusetts Institute of Technology (Free video)
  - Programming in Modern C++ — IIT Kharagpur (Free course)
2. 02.2Core Python
  CoreIntermediate6–10 h
  Python language questions for Python roles.
  What this covers
  - Data types — Mutability, lists vs tuples, dicts and sets.
  - Functions — args, kwargs, closures, decorators.
  - Iterators & generators — yield and lazy evaluation.
  - OOP in Python — Classes, dunder methods, inheritance.
  - Error handling — Exceptions and context managers.
  - Performance — The GIL, list comprehensions, profiling.
  What to practise
  - Write a decorator that times a function
  - Solve 15 problems in idiomatic Python
  Common questions
  - What is a decorator?
  - Difference between a list and a tuple?
  - What is the GIL?
  Free resources
  - Harvard CS50x — The best first CS course there is (Free)
  - Exercism — Practice exercises with human mentoring (Free)
  - Kaggle Learn — Short practical courses: Python, Pandas, ML, SQL (Free)
  Free courses
  - CS50's Introduction to Programming with Python (CS50P) 2022 — Harvard University (Free video)
  - MIT 6.100L Introduction to CS and Programming using Python, Fall 2022 — Massachusetts Institute of Technology (Free video)
  - CS50's Web Programming with Python and JavaScript (CS50W) 2020 — Harvard University (Free video)
3. 02.3Data structures & algorithms
  Good to knowIntermediate40–80 h
  The core problem-solving topics commonly assessed in software interviews.
  What this covers
  - Arrays & hashing — Frequency maps, prefix sums, duplicates.
  - Two pointers & sliding window — Pairs, subarrays and substrings in O(n).
  - Stacks & queues — Brackets, monotonic stacks, BFS queues.
  - Linked lists — Reversal, cycle detection, merging.
  - Trees & BST — Traversals, height, LCA, validation.
  - Graphs — BFS, DFS, topological sort, shortest paths.
  - Binary search — On sorted arrays and on the answer.
  - Dynamic programming — 1D and 2D DP, from climbing stairs to knapsack.
  What to practise
  - Follow one list end to end, not five
  - Say the brute force first, then improve it
  - One timed problem a day
  Common questions
  - What is the difference between BFS and DFS?
  - How does a hash map handle collisions?
  - Find the first non-repeating character in a string.
  Free resources
  - NeetCode 150 — A curated ordered list instead of 3000 random problems (Free)
  - Striver's A2Z DSA sheet — India's most-followed DSA sheet (Free)
  - LeetCode — The default interview practice set (Free plan)
  - GeeksforGeeks — Company-wise questions and theory (Free plan)
  Free courses
  - Data Structures and Algorithms Design — IIT Kanpur (Free course)
  - MIT 6.006 Introduction to Algorithms, Spring 2020 — Massachusetts Institute of Technology (Free video)
  - Neetcode 150 Course - All Coding Interview Questions Solved — freeCodeCamp (Free video)
03ML foundations
What sits under LLM apps.
1. 03.1Machine learning fundamentals
  CoreIntermediate15–25 h
  Core ML concepts asked in data scientist and ML interviews.
  What this covers
  - Supervised learning — Regression, classification, common algorithms.
  - Bias–variance — Underfitting, overfitting, regularisation.
  - Evaluation — Train/test splits, cross-validation, precision, recall, ROC.
  - Feature engineering — Encoding, scaling, leakage.
  - Trees & ensembles — Random forests and gradient boosting.
  - Unsupervised learning — Clustering and dimensionality reduction.
  What to practise
  - Train and evaluate a model on a public dataset
  - Explain precision vs recall with your own example
  Common questions
  - What is overfitting and how do you prevent it?
  - When would you use precision over recall?
  - How does a random forest work?
  Free resources
  - Machine Learning Crash Course — Google's free introduction to machine learning (Free)
  - StatQuest — Statistics and ML explained clearly, in videos (Free)
  - Kaggle Learn — Short practical courses: Python, Pandas, ML, SQL (Free)
  - Machine Learning Interviews book — Free book on ML interview questions and process (Free)
  Free courses
  - Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018 — Stanford University (Free video)
  - Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati) — Stanford University (Free video)
  - Stanford CS229: Machine Learning I Spring 2022 — Stanford University (Free video)
2. 03.2Deep learning
  Good to knowAdvanced15–25 h
  Neural networks, training and modern architectures.
  What this covers
  - Neural network basics — Layers, activations, backpropagation.
  - Training — Loss functions, optimisers, learning rates.
  - Regularisation — Dropout, batch norm, data augmentation.
  - CNNs — Convolutions for images.
  - Transformers — Attention and why it works.
  - Frameworks — PyTorch or TensorFlow basics.
  What to practise
  - Train a small CNN and explain its errors
  - Implement a tiny neural net from scratch
  Common questions
  - What is backpropagation?
  - Why do we need activation functions?
  - What is attention?
  Free resources
  - fast.ai — Practical deep learning, top-down (Free)
  - Andrej Karpathy — Zero to Hero — Build a neural net and a GPT from scratch (Free)
  - Hugging Face Learn — NLP, LLM, agents and diffusion courses (Free)
  - DeepLearning.AI short courses — Short, focused LLM and agent courses (Free plan)
  Free courses
  - Stanford CS224N: Natural Language Processing with Deep Learning | Winter 2021 — Stanford University (Free video)
  - Stanford CS224N: Natural Language Processing with Deep Learning Course | Winter 2019 — Stanford University (Free video)
  - MIT 6.7960 Deep Learning, Fall 2024 — Massachusetts Institute of Technology (Free video)
3. 03.3Natural language processing
  Good to knowAdvanced10–15 h
  Text representations, transformers and evaluating language models.
  What this covers
  - Text preprocessing — Tokenisation, subwords.
  - Embeddings — Word and sentence vectors.
  - Transformers — Encoders, decoders, attention.
  - Tasks — Classification, NER, summarisation, QA.
  - Evaluation — Accuracy, F1, BLEU/ROUGE and their limits.
  What to practise
  - Fine-tune a small text classifier
  - Compare two embedding models on a task
  Common questions
  - How does a transformer work?
  - What is tokenisation?
  Free resources
  - Hugging Face Learn — NLP, LLM, agents and diffusion courses (Free)
  - DeepLearning.AI short courses — Short, focused LLM and agent courses (Free plan)
  - Andrej Karpathy — Zero to Hero — Build a neural net and a GPT from scratch (Free)
  Free courses
  - Stanford CS224N: Natural Language Processing with Deep Learning | Winter 2021 — Stanford University (Free video)
  - Stanford CS224N: Natural Language Processing with Deep Learning Course | Winter 2019 — Stanford University (Free video)
  - Deep Learning for Natural Language Processing — IIT Kharagpur (Free course)
04AI engineering
The main role-specific rounds.
1. 04.1LLM applications
  CoreAdvanced8–12 h
  Building products on top of large language models and evaluating them.
  What this covers
  - Prompting — Instructions, examples, structured outputs.
  - Retrieval (RAG) — Chunking, embeddings, vector search.
  - Evaluation — Test sets, human review, failure analysis.
  - Fine-tuning basics — When it helps and when it does not.
  - Safety & cost — Hallucinations, privacy, latency, tokens.
  What to practise
  - Build a small RAG demo over your own notes
  - Write an evaluation set of 20 cases for it
  Common questions
  - How does retrieval-augmented generation work?
  - How do you evaluate an LLM feature?
  Free resources
  - DeepLearning.AI short courses — Short, focused LLM and agent courses (Free plan)
  - Hugging Face Learn — NLP, LLM, agents and diffusion courses (Free)
  - Andrej Karpathy — Zero to Hero — Build a neural net and a GPT from scratch (Free)
  Free courses
  - Large Language Models (LLMs) — Stanford University (Free video)
  - Create a large language model deployment — Microsoft (Free course)
  - Introduction to large language models — Microsoft (Free course)
2. 04.2Back-end & API design
  CoreIntermediate8–12 h
  Designing, securing and scaling the APIs your projects expose.
  What this covers
  - REST design — Resources, verbs, status codes, versioning.
  - Authentication — Sessions, JWT, OAuth at a basic level.
  - Validation & errors — Consistent error shapes and input checks.
  - Pagination & filtering — Cursor vs offset pagination.
  - Rate limiting & idempotency — Protecting APIs and safe retries.
  - One framework deeply — Express, Spring Boot, Django or FastAPI.
  What to practise
  - Build a CRUD API with auth and tests
  - Write the API docs for one of your projects
  Common questions
  - How would you design a REST API for a library?
  - What is idempotency?
  - How does JWT authentication work?
  Free resources
  - Full Stack Open — University of Helsinki's React/Node course (Free)
  - Postman Student Expert — API skills badge, quick and respected (Free)
  - MDN Web Docs — The reference for HTML, CSS and JavaScript (Free)
  - System Design Primer — Open-source guide to designing large systems (Free)
  Free courses
  - Rest Api | Restful Web Service — Telusko (Free video)
  - PHP REST API From Scratch — Traversy Media (Free video)
  - Rest API Using Spring — Telusko (Free video)
3. 04.3MLOps
  Good to knowAdvanced8–12 h
  Shipping and running ML models reliably.
  What this covers
  - Experiment tracking — Reproducible runs and versioned data.
  - Pipelines — Training and evaluation automation.
  - Model serving — APIs, batch jobs, scaling.
  - Monitoring — Data drift and model decay.
  - CI/CD for ML — Testing data and models.
  What to practise
  - Serve a model behind an API in a container
  - Add drift monitoring to a toy model
  Common questions
  - How do you know a model in production is degrading?
  - How would you version datasets?
  Free resources
  - DeepLearning.AI short courses — Short, focused LLM and agent courses (Free plan)
  - Docker getting started — Official step-by-step guide to containers (Free)
  - Machine Learning Interviews book — Free book on ML interview questions and process (Free)
  Free courses
  - Introduction to machine learning operations (MLOps) — Microsoft (Free course)
  - Operationalize machine learning models (MLOps) — Microsoft (Free course)
  - MLOps Course – Build Machine Learning Production Grade Projects — freeCodeCamp (Free video)
4. 04.4System design basics
  Good to knowAdvanced10–20 h
  More common for experienced roles; some fresher rounds touch it.
  What this covers
  - Client–server basics — Requests, APIs, stateless servers.
  - Load balancing — Spreading traffic across servers.
  - Caching — What to cache, where, and invalidation.
  - Databases at scale — SQL vs NoSQL, replication, sharding.
  - Queues — Async work and decoupling.
  - Classic problems — URL shortener, chat, news feed.
  What to practise
  - Design a URL shortener on paper
  - Explain trade-offs, not just boxes
  Common questions
  - Design a URL shortener.
  - How would you scale a read-heavy app?
  Free resources
  - System Design Primer — Open-source guide to designing large systems (Free)
  - GeeksforGeeks — Company-wise questions and theory (Free plan)
  Free courses
  - System Design Concepts Course and Interview Prep — freeCodeCamp (Free video)
5. 04.5Projects & resume defense
  CoreIntermediate4–6 h
  Explaining what you built, why, and what you would change.
  What this covers
  - The problem — What it solves and for whom.
  - Your role — Exactly what you built yourself.
  - Architecture — A simple diagram: frontend, backend, database.
  - Hardest bug — What broke and how you found it.
  - Trade-offs — Why this stack; what you would change now.
  - Scale questions — What happens with 10x the users.
  What to practise
  - Prepare a 2-minute walkthrough per project
  - Be ready to draw its architecture
  Common questions
  - What was the hardest part of this project?
  - Why did you choose this tech stack?
  - How would it handle 10x more users?
  Free resources
  - Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
  - Pramp / Exponent peer mocks — Free peer-to-peer mock interviews (Free plan)
  Free courses
  - Software Engineering Concepts — MIT OpenCourseWare (Free course)
  - Build community-driven software projects on GitHub — Microsoft (Free course)
05Practice
Saying it out loud is a different skill.
1. 05.1Technical mock interviews
  CoreIntermediate3–6 h
  At least two full coding mocks before the real one.
  What this covers
  - Thinking aloud — Narrate your approach before and while coding.
  - Clarifying questions — Confirm inputs, scale and edge cases first.
  - Handling hints — Take them gracefully and adapt.
  - "I don't know" — Say what you do know and how you would find out.
  - Review the recording — Fillers, pace, unclear explanations.
  What to practise
  - Do two peer mock interviews
  - Record one spoken mock and listen back
  Common questions
  - Walk me through your approach before you code.
  - How would you test this?
  Free resources
  - Pramp / Exponent peer mocks — Free peer-to-peer mock interviews (Free plan)
  - Google Interview Warmup — Free spoken mock interview that prints your transcript (Free)
  - Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
  Free courses
  - Software Engineering Job Interview – Full Mock Interview — freeCodeCamp (Free video)
2. 05.2Technical communication
  Good to knowBeginner2–3 h
  Explaining your thinking clearly in English.
  What this covers
  - Structure — Big picture first, then details.
  - Plain words — Explain without jargon.
  - Checking in — "Does that make sense so far?"
  - Written English — Clear emails and follow-ups.
  What to practise
  - Explain one project to a non-CS friend
  - Record a 2-minute explanation of your best project and listen back
  Common questions
  - Explain recursion to a 10-year-old.
  Free resources
  - BBC Learning English — Structured free English lessons and podcasts (Free)
  - LanguageTool — Grammar checking that isn't Grammarly (Free plan)
  Free courses
  - Technical Communications for Engineers — IIT Roorkee (Free course)
06Company & final prep
The last 48 hours.
1. 06.1Company research
  CoreBeginner1–2 h
  The product, the role, the job description and recent news.
  What this covers
  - The business — What it sells and to whom.
  - The role — What the job description really asks for.
  - Recent news — Launches, results, announcements.
  - Interview format — The rounds, if the company shares them.
  - Your fit — Three reasons, matched to the job description.
  What to practise
  - Write 3 reasons you want this role
  - Match 3 JD lines to your experience
  Common questions
  - Why do you want to work here?
  - What do you know about our product?
  Free resources
  - GeeksforGeeks — Company-wise questions and theory (Free plan)
  - Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
2. 06.2Questions to ask
  Good to knowBeginner30 min
  Two or three good questions for the end of the interview.
  What this covers
  - About the team — How work is planned and reviewed.
  - About growth — Mentoring, learning, the first 90 days.
  - About the product — What is hard right now.
  - What to avoid — Questions a quick search answers.
  What to practise
  - Write 5 questions, keep the best 3
  - Prepare one question about the team's own product
  Common questions
  - Do you have any questions for us?
  Free resources
  - Tech Interview Handbook — Free guide to coding and behavioral interviews (Free)
3. 06.3Final checklist
  CoreBeginner30 min
  Logistics, documents, setup and a calm night before.
  What this covers
  - Logistics — Time, timezone, link or address.
  - Documents — Resume copies, ID, certificates if asked.
  - Tech check — Camera, mic, internet, charger.
  - Night before — Sleep, light revision, no new topics.
  What to practise
  - Do a 5-minute tech check the day before
  - Lay out documents and clothes the night before
  Free resources
  - Google Interview Warmup — Free spoken mock interview that prints your transcript (Free)
4. 06.4Job & interview safety
  Good to knowBeginner15 min
  Spot fake offers, payment requests and fake interview links.
  What this covers
  - Payment requests — Real employers don't charge for jobs or training.
  - Email domains — Check it matches the company's own site.
  - OTPs & logins — Never share them with anyone.
  - Fake interview links — Care with unknown chat apps and downloads.
  - Reporting — The 1930 helpline and cybercrime.gov.in.
  What to practise
  - Check one recent offer against these rules
  - Confirm the opening is listed on the company's own careers page
  Free resources
  - National Cyber Crime Portal — Report online fraud and job scams (Free)
  - Have I Been Pwned — Check if your email appears in a data breach (Free)

AI Engineer

01Interview foundations

01.1Resume

What this covers

What to practise

Common questions

Free resources

Free courses

01.2Tell me about yourself

What this covers

What to practise

Common questions

Free resources

Free courses

01.3Behavioral & STAR

What this covers

What to practise

Common questions

Free resources

Free courses

02Coding

02.1Programming fundamentals

What this covers

What to practise

Common questions

Free resources

Free courses

02.2Core Python

What this covers

What to practise

Common questions

Free resources

Free courses

02.3Data structures & algorithms

What this covers

What to practise

Common questions

Free resources

Free courses

03ML foundations

03.1Machine learning fundamentals

What this covers

What to practise

Common questions

Free resources

Free courses

03.2Deep learning

What this covers

What to practise

Common questions

Free resources

Free courses

03.3Natural language processing

What this covers

What to practise

Common questions

Free resources

Free courses

04AI engineering

04.1LLM applications

What this covers

What to practise

Common questions

Free resources

Free courses

04.2Back-end & API design

What this covers

What to practise

Common questions

Free resources

Free courses

04.3MLOps

What this covers

What to practise

Common questions

Free resources

Free courses

04.4System design basics

What this covers

What to practise