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Brindha
Aug 02, 2026
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Stop Memorizing, Start Understanding: A Free AI Engineering Interview Repo

Grounded / Real Inflated / Uruttu
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60% uruttu
article Original Content
Most AI interview preparation goes wrong for one reason. People memorize answers. Interviewers don't care if you can define RAG, Docker, or LangGraph. They want to know whether you understand why, when, and how these technologies are used in production. After months of preparing, taking interviews, and creating AI engineering content, I realized I kept writing the same explanations over and over. So I decided to organize everything into one place. Introducing Roy's AI Lab – AI Engineer Interview Q&A 🧠 A free GitHub repository covering interview questions from fundamentals to production-level AI engineering. Inside you'll find: 📘 Python → Core concepts to advanced topics → OOP, decorators, generators, async → Interview-focused explanations ⚡ FastAPI & Backend → APIs, dependency injection, validation → Async programming → Production-ready backend concepts 🤖 AI & Machine Learning → Machine Learning → Deep Learning → Transformers → RAG → AI Agents & Agentic AI 🔗 LLM Frameworks → LangChain → LangGraph → Real-world interview scenarios → Production design questions ⚙️ Engineering & DevOps → Git & GitHub → Docker → Kubernetes → MLOps → AWS for AI 🎯 Every topic includes → 15–20 interview questions → Basic → Advanced progression → Production-level discussions → Code examples where relevant The goal isn't to help you memorize answers. It's to help you understand the reasoning behind them because that's what interviewers actually evaluate. I'll continue adding new topics and updating existing ones as I learn. ⭐ If you find it useful, consider starring the repository. 💬 Link to the repository 👉 https://lnkd.in/gFbAPTym Follow Ritesh Rai & Roy's AI Lab for more AI Engineering content. #Day154 of Documenting my Learnings & Building Meaningful Connections on LinkedIn.
verified Validated Content

I wasn't able to independently verify the specific repository this post promotes.

Could not confirm:

  • I could not locate a GitHub repository or LinkedIn company page specifically matching "Roy's AI Lab" by "Ritesh Rai" covering the described content (Python, FastAPI, RAG, LangChain/LangGraph, Docker, Kubernetes, MLOps, AWS interview Q&A).
  • The shortened link (lnkd.in/...) can't be accessed directly — LinkedIn's redirect links block automated fetching, so I couldn't verify the destination repo's actual content, star count, or structure.
  • A GitHub user named "Ritesh-roy" does exist, but their public profile shows work in full-stack MERN development (React, Next.js, Node.js, MongoDB) and SEO — not an AI engineering interview repo. This may or may not be the same person; I can't confirm either way.

What is independently verifiable and plausible:

  • The type of resource described (a free, community-built GitHub repo of AI/ML engineering interview questions covering Python, RAG, LangChain, LangGraph, Docker, Kubernetes, MLOps) is a very common and well-established genre on GitHub right now — I found numerous comparable, verified repositories doing exactly this (e.g., KalyanKS-NLP/RAG-Interview-Questions-and-Answers-Hub, amitshekhariitbhu/ai-engineering-interview-questions, alexeygrigorev/ai-engineering-field-guide). So the described project is entirely consistent with a real and active trend, even though I couldn't confirm this exact instance.
  • The core argument — that interviewers evaluate reasoning about when/why to use tools like RAG or Docker rather than rote definitions — is a reasonable, widely-echoed take in technical interviewing generally, not a factual claim requiring a citation.

Verdict: I can't confirm or deny that this specific repository exists as described, since I have no way to browse the actual linked page. Nothing about the claim is implausible — it fits a well-populated genre of real projects — but the specific person, repo, and its exact contents are unverified from my side. I'd recommend checking the link yourself before amplifying specific claims (like exact topic counts or "15–20 questions per topic").