E2E Python & AI Interview Preparation

Welcome to the ultimate, end-to-end interview preparation repository. This repository is specifically tailored for Senior/MAANG-level interviews focusing on Python, System Design, Machine Learning, Deep Learning, Generative AI, and MLOps.

Objective: A comprehensive, zero-dependency resource. You do not need to look elsewhere to prepare for your interviews. Every concept is broken down into 🟢 Simple, 🟡 Medium, and 🔴 Hard tiers.

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🗂️ Master Curriculum Index

Readiness Audit

• Interview Prep Repository Audit

Core Engineering

1. Python Core
   • Theory & Internals | Advanced Concepts (GIL, Memory) | Coding Patterns
2. DSA & Design Patterns
   • DSA Theory | DSA Coding Implementation
   • Software Design Patterns
3. Async & Concurrency
   • AsyncIO & Multiprocessing Theory

Backend & Databases

4. SQL & Databases
   • SQL Theory & Window Functions | SQL Coding Problems
   • Database Internals (B-Trees, ACID)
5. Caching & Messaging
   • Redis & Caching Strategies | Redis Coding
   • Message Queues (Kafka, RabbitMQ)
6. Modern Python Web Stack
   • FastAPI Theory | FastAPI Coding
   • Pydantic V2 (Rust Core) | Pydantic Coding
   • SQLAlchemy 2.0 (ORM & Async) | SQLAlchemy Coding (Async & N+1)
   • API Design (REST, Webhooks, SSE) | API Design Coding
7. Testing & Pytest
   • Testing Theory (Pyramid & Fixtures) | Testing Coding (Mocking & TestClient)

System Design

8. Core System Design
   • Theory (CAP, Load Balancing)
   • Architectural Problems (TinyURL, Netflix)
9. AI System Design
   • ML System Design (RecSys, Fraud)
   • RAG System Design
   • Agentic AI System Design

Machine Learning & Data Science

10. Classical ML
    • ML Theory (Bias/Variance, Ensembles) | ML Coding (NumPy)
11. Statistics, Probability & Experimentation
    • Statistics, Probability, and Experimentation for ML Interviews
12. Deep Learning
    • DL Theory (CNNs, LSTMs, Adam) | DL Coding (PyTorch)
    • Model Architecture Families (CNNs, RNNs, LLMs, Diffusion, Image Models)
13. Natural Language Processing (NLP)
    • NLP Theory (Attention, BERT/GPT) | NLP Coding (Self-Attention)
    • Transformer Architecture Deep Dive

Generative AI & Agentic AI

14. LLM Ecosystem
    • Hugging Face Theory (PEFT, Quantization) | Hugging Face Coding (LoRA)
    • Prompt Engineering (CoT, ToT)
    • LangChain Theory & LCEL | LangChain Coding
    • LangGraph Theory | LangGraph Coding (ReAct loops)
15. Generative AI Architecture
    • Gen AI Theory (RLHF, MoE, KV Cache)
    • RAG Real-World Scenarios
    • Advanced Topics (RoPE, DSPy, LLaVA, KV Cache Math)
    • LLM Optimization, Fine-Tuning, PEFT, Quantization, and Evaluation
16. Agentic AI Architectures
    • Agentic Theory (ReAct vs Plan/Execute)
    • Agentic Real-World Scenarios

Information Retrieval & Evaluation

17. Vector Databases & Evaluation
    • Vector DB Theory (HNSW, Hybrid Search, ColBERT)
    • Chunking Strategies
    • Evaluation Frameworks (RAGAS, LLM-as-a-Judge) | Evaluation Coding (Trajectories)

Deployment & Infrastructure

18. MLOps & Cloud
    • MLOps Theory (MLflow, Data Drift)
    • Docker Theory (Images, Layers)
    • Kubernetes Theory (Pods, HPA)
    • Deployment Strategies (Blue-Green, ONNX)
    • GCP for ML (Vertex AI, BQML) | GCP Coding (Vertex AI)
    • AWS vs Azure vs GCP for ML, GenAI, Agents, Deployment, and Storage

Leadership & Soft Skills

19. Behavioral & Leadership
    • The STAR Method & Situational Questions

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Good luck with your interviews! You have everything you need right here.