Companion to the AI Engineering Roadmap 2026 LinkedIn Newsletter

"Knowing how to build is half the battle. Knowing how to explain what you built — that's what gets you hired."

What Is This?

A MAANG-level interview preparation series for AI Engineer roles at companies like Google, Meta, OpenAI, Anthropic, Microsoft, Amazon, and top AI startups.

Each week maps directly to an episode of the AI Engineering Roadmap 2026 newsletter. Each folder contains 4 interview prep files covering every angle interviewers attack from:

File What It Covers
01_Deep_Conceptual_Questions.md Theory, intuition, "explain why" — separates memorizers from thinkers
02_Technical_Coding_Questions.md Live coding, debugging, implementation — hands-on-keyboard questions
03_System_Design_Questions.md Architecture, scaling, trade-offs — whiteboard-level AI system design
04_Behavioral_Scenario_Questions.md "What would you do if…" — production incidents, team dynamics, real-world judgment

Plus a comprehensive Extra Prep Questions file covering 65+ cross-cutting topics that span multiple weeks.

Weekly Index

Week Topic Repo Status
1 LLM Fundamentals Understanding_LLMs_From_The_Inside_Out ✅
2 Python for AI Python_For_AI_What_Actually_Matters ✅
3 Tool Calling, APIs & Validation Building_AI_Project_Blueprint_for_Beginners ✅
4 End-to-End AI Projects Your_First_End_To_End_AI_Project ✅
5 RAG — Connecting AI to Your Data Coming soon 🔜
6 Frameworks — LangChain, LlamaIndex, LangGraph, CrewAI Coming soon 🔜
7 Memory & State in AI Systems and many more Coming soon 🔜

Bonus: Extra Prep Questions — 65+ deep-dive questions covering RAG architecture, embeddings, vector DBs, agents, fine-tuning, prompt engineering, evaluation, enterprise security, inference optimization, observability, multi-tenancy, and more.

Question Count

Section Questions
Week 1 — LLM Fundamentals 45
Week 2 — Python for AI 36
Week 3 — Tool Calling, APIs & Validation 41
Week 4 — End-to-End AI Projects 65
Extra Prep (cross-cutting topics) 70+
Total 257+

How to Use This

If you're preparing for interviews:

  • Start with the week that matches your weakest area
  • For each question, try answering BEFORE reading the answer
  • Pay attention to the "What the interviewer is really testing" notes
  • Practice the follow-up questions — interviewers always go deeper
  • Use the Extra Prep file for cross-cutting topics that come up in every interview

If you're a hiring manager:

  • Use these as a question bank for AI engineer screenings
  • The difficulty ratings help calibrate for junior vs senior roles
  • System design questions work well for 45-minute whiteboard rounds

If you're self-studying:

  • Pair each prep file with the matching GitHub repo to build while you learn
  • The coding questions can be practiced as standalone exercises

Difficulty Scale

Level Meaning
⭐ Entry-level / new grad — fundamentals
⭐⭐ Mid-level — applied knowledge
⭐⭐⭐ Senior — architecture + trade-offs
⭐⭐⭐⭐ Staff+ / MAANG bar — deep expertise + system thinking

The AI Job Market Reality (2025-2026)

The market has bifurcated hard:

Role Type Status Comp Trend
Generic "AI Engineer" Saturated Down 20-30% from peak
Specialized (evals, fine-tuning, inference optimization, RAG) Starved for talent Holding or up

What gets you the interview:

  • ❌ "Built RAG pipeline with LangChain and Pinecone"
  • ✅ "Reduced hallucination rate from 34% to 8% on a 50K-doc corpus"

This prep series teaches you to think and talk like the second person.

Part of the AI Engineering Roadmap 2026

📬 Subscribe to the newsletter — it's free, weekly, and each episode pairs a LinkedIn post with a production-grade GitHub repo.

Also check out the companion interview prep newsletter:

📬 AI Engineer Interview Prep Newsletter

If this helped you land an interview or get an offer, give the repo a ⭐ and share your story.