shafaypro/CrackingMachineLearningInterview

A repository to prepare you for your machine learning interview, involving most of the questions asked by all the tech giants and local companies. Do this to Ace your Machine Learning Engineer Interviews

What it solves

Preparing for machine learning, AI, data science, and related engineering interviews is overwhelming because topics span from math and algorithms to production systems, cloud platforms, and LLM-specific techniques. This repository organizes a massive body of interview prep material into role-specific tracks, so you can focus on what actually gets asked for your target job.

How it works

You start by picking a track (ML Engineer, Data Scientist, Data Engineer, MLOps, AI/GenAI Engineer, Deep Learning, DevOps, etc.) via the "Choose Your Track" guide, which gives you an ordered path: prerequisites, stage-by-stage reading order, a project to build, and signs of interview readiness. The repo then provides curated notes across many subfolders: classical ML, deep learning, AI/GenAI (including RAG, agents, LLM inference, LangGraph), MLOps, data engineering, DevOps, cloud platforms, system design, coding challenges, and project setup. It also includes practical tools like a one-page ML cheat sheet, a glossary with flashcards, debugging scenarios, and a 2026 interview roadmap with question banks. A helper script generates a searchable website, and links are checked automatically in CI.

Who it’s for

Machine Learning Engineers, Data Scientists, Deep Learning Engineers, AI/GenAI Engineers, Data Engineers, MLOps/DevOps/Platform Engineers, and software engineers working on AI/ML products. It suits both newcomers (via the track guide) and experienced candidates targeting senior or staff roles.

Highlights

  • Role-based tracks with ordered reading paths and a project to build for each role
  • Covers classic ML, deep learning, LLM/GenAI (RAG, agents, inference), MLOps, data engineering, DevOps, cloud platforms, and system design
  • Includes 2026-specific interview roadmaps, question banks, and additional questions for modern rounds
  • Interactive glossary flashcards and a concise ML cheat sheet for quick review
  • Debugging scenario guides (e.g., data leakage, NaN loss, drift) with ranked causes and fixes
  • Take-home project guides that explain what reviewers score, time budgeting, and presentation tips
  • Browser-based flashcards save progress locally
  • Offline link checker and a site builder tool are included (run in CI)

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