A comprehensive collection of machine learning and AI interview preparation materials, covering ML coding, system design, LLM/GenAI, and DSA.
This repository contains battle-tested interview preparation materials for ML/AI engineering roles, including:
- Machine Learning: Algorithms from scratch, coding problems, production ML systems
- LLM/GenAI: Production LLMs, RAG systems, embeddings, attention mechanisms
- System Design: Agentic AI systems, distributed ML, scalable architectures
- DSA: Data structures and algorithms for ML engineers
- MLOps: Production best practices, monitoring, deployment
- Start with ML Coding Interview Master Guide
- Practice with ML Algorithms from Scratch
- Review Neural Network Components
- Read LLM/ML System Design Master Guide
- Study iterative examples:
- Study Model Context Protocol (MCP) - Universal AI-tool integration standard
- Review System Design Examples
- Start with fundamentals:
- LLM Production Complete Guide
- Production RAG Systems
- Embedding Models Guide
- LoRA/QLoRA Fine-tuning
- Model Context Protocol (MCP):
- ML Coding Interview Master Guide - Complete guide to ML coding interviews
- ML Algorithms from Scratch - Implement core ML algorithms:
- Linear Regression, Logistic Regression
- Decision Trees, Random Forest
- K-Means, KNN
- Naive Bayes, SVM
- Gradient Descent variants
- ML Coding Problems - Practice problems with solutions
- Decision Trees Complete Guide - Complete guide to decision trees:
- Tree construction algorithms (CART, ID3, C4.5)
- Splitting criteria (Entropy, Gini, Information Gain)
- Pruning techniques (pre-pruning, post-pruning)
- Regression trees and variance reduction
- Feature importance (MDI, permutation)
- Tree visualization methods
- Implementation from scratch
- 25+ interview questions with answers
- Bagging Ensemble Methods - Bootstrap Aggregating complete guide:
- Bootstrap sampling, OOB error
- Random Forest deep dive
- Variance reduction mechanism
- Implementation from scratch
- 20+ interview questions with answers
- Boosting Ensemble Methods - Boosting algorithms complete guide:
- AdaBoost, Gradient Boosting
- XGBoost, LightGBM, CatBoost
- Bias reduction mechanism
- Algorithm comparison and when to use each
- 25+ interview questions with answers
- MLOps Production Guide - End-to-end ML in production:
- Model versioning, experiment tracking
- CI/CD for ML, model deployment
- Monitoring, A/B testing
- Data pipelines, feature stores
- Feature Engineering Guide - Feature engineering techniques:
- Numerical, categorical, text features
- Time series, embeddings
- Feature selection, dimensionality reduction
- Neural Network Components - Build neural networks from scratch:
- Dense layers, activation functions
- Backpropagation, optimizers (SGD, Adam, RMSprop)
- Batch normalization, dropout
- CNN components
- Attention Mechanisms Guide - Deep dive into attention:
- Self-attention, multi-head attention
- Transformer architecture
- Positional encoding
- BERT, GPT architectures
- Transformer Architecture Complete Guide - Transformer fundamentals:
- Encoder-only, Decoder-only, Encoder-Decoder
- Pretraining & Post-training (SFT, RLHF, DPO, PPO)
- Scaling laws
- LLM Fundamentals Part 1: Tokenization & Context - Core LLM concepts:
- Tokenization (BPE, WordPiece, SentencePiece, Unigram)
- Context windows and memory complexity
- Positional encoding (Sinusoidal, RoPE, ALiBi)
- Complete implementations with mermaid diagrams
- LLM Fundamentals Part 2: Inference & Optimization - Advanced LLM concepts:
- Inference strategies (greedy, beam search, sampling, temperature, top-k, top-p)
- Evaluation metrics (perplexity, task-specific)
- Model sizes and scaling laws (Chinchilla, Kaplan)
- KV cache optimization (PagedAttention, quantization)
- Speculative decoding (2-4× speedup)
- Prompting techniques (zero-shot, few-shot, CoT, self-consistency)
- LLM Production Complete Guide - Production LLMs:
- Model selection, deployment strategies
- Cost optimization, caching
- Prompt engineering, fine-tuning
- Evaluation metrics
- LoRA/QLoRA Fine-tuning - Parameter-efficient fine-tuning:
- LoRA, QLoRA concepts
- Implementation examples
- Quantization techniques
- Memory optimization
- Production RAG Systems - Building RAG systems:
- Document chunking, indexing
- Hybrid search, re-ranking
- Evaluation (Ragas, TruLens)
- Advanced RAG patterns
- Embedding Models Guide - Embeddings in depth:
- Model selection (OpenAI, Cohere, sentence-transformers)
- Semantic search, clustering
- Fine-tuning embeddings
- Vector databases
- LLM/ML System Design Master Guide - Complete framework:
- Interview approach (45-minute structure)
- Key patterns (RAG, agents, fine-tuning)
- Production considerations
- Evaluation strategies
- Agentic AI Customer Support - Build from scratch:
- 10 iterations: bare minimum → production
- Multi-agent orchestration (LangGraph)
- Memory & context management
- Scale to 100K+ queries/day
- Cost optimization ($5K → $220/day)
- AI Code Review System - Iterative design:
- 10 iterations: single LLM → production
- RAG for codebase context
- Multi-agent specialists (security, performance, bugs)
- Learning from developer feedback
- Scale to 500 PRs/day
- Agent Memory Architecture - Memory patterns:
- 6 memory types (short-term, long-term, episodic, semantic, entity, procedural)
- Hybrid memory architecture (4 layers)
- Framework comparison (Anthropic, LangGraph, CrewAI, OpenAI Swarm)
- Semantic caching (40-70% cost reduction)
- MCP Interview Preparation Guide - Complete MCP interview prep:
- MCP architecture and three core primitives
- Client-Host-Server architecture patterns
- Transport layers (stdio, HTTP+SSE)
- Production deployment patterns
- Security best practices (mTLS, authentication)
- Industry adoption timeline
- 10 comprehensive Q&A sections
- 25+ mermaid diagrams
- MCP Hands-On Implementation - Practical MCP coding:
- Building MCP servers with custom tools
- Implementing resource providers
- Creating prompt templates
- GitHub and database integration examples
- Complete end-to-end projects
- MCP Enterprise Banking Use Case - Real-world enterprise example:
- Banking mainframe integration with cloud AI agents
- Zero-trust security with data residency compliance
- Tokenization, encryption, HSM integration
- PCI-DSS, SOX, GDPR, GLBA compliance
- Multi-agent orchestration for fraud detection
- Complete ROI analysis (700% ROI)
- MCP Production Best Practices - Production deployment guide:
- High-availability MCP server clusters
- Multi-region deployment architecture
- Serverless MCP on AWS Lambda
- Security (mTLS, secrets management)
- Monitoring and observability
- Circuit breakers, retries, graceful degradation
- Cost optimization strategies
- Complete production checklist
- System Design Examples Enhanced - ML system designs:
- Threat detection system
- Semantic search
- Network anomaly detection
- Complete with architecture diagrams
- DSA Learning Plan - 6-day crash course
- Day 1: Arrays & Searching
- Day 2: Sorting Algorithms
- Day 3: Two Pointers
- Day 4: Recursion & Backtracking
- Day 5: Hash Maps & Sets
- Day 6: Practice Problems
- Bonus: Advanced Patterns
- Interview Prep Complete Index - Master index
- Master Study Schedule - Week-by-week plan
- Notebook Guide - How to use Jupyter notebooks
- Questions for Interviewers - Smart questions to ask
- Technical Cheatsheet - Quick reference
- Leadership Stories Template - STAR method examples
- LangChain Learning Series - Comprehensive 8-module course on LangChain from fundamentals to production deployment (separate repository)
Week 1: ML Fundamentals
- Days 1-3: ML algorithms from scratch
- Days 4-5: Neural network components
- Days 6-7: ML coding problems
Week 2: LLM/GenAI
- Days 1-2: LLM fundamentals (tokenization, context, inference)
- Days 3-4: LLM production guide
- Days 5: RAG systems
- Days 6: Embeddings & attention
- Day 7: Fine-tuning (LoRA/QLoRA)
Week 3: System Design
- Days 1-2: System design framework
- Days 3-4: Agentic AI examples (iterative)
- Days 5-6: Traditional system design
- Day 7: Practice mock interviews
Week 4: DSA + Review
- Days 1-6: DSA crash course (one topic per day)
- Day 7: Full mock interview
Week 1: ML + LLM
- Days 1-2: ML coding essentials
- Days 3: LLM fundamentals (tokenization, context, inference)
- Days 4-5: LLM production basics
- Day 6: RAG systems
- Day 7: System design framework
Week 2: System Design + DSA
- Days 1-3: System design examples
- Days 4-6: DSA essentials
- Day 7: Mock interviews
Unlike traditional system design resources, this repo uses an iterative interview approach:
- Start with bare minimum (10 lines of code)
- Add complexity step-by-step (10 iterations)
- Discuss tradeoffs at each step
- Show production evolution (cost, scale, monitoring)
Example: Agentic AI system goes from:
- Iteration 1: Single LLM ($5/query)
- → Iteration 7: Model routing + caching ($0.0022/query)
- 2,272x cost reduction with detailed reasoning at each step!
- Not just theory - working code for everything
- Jupyter notebooks with runnable examples
- Production-ready patterns and architectures
- Real-world tradeoffs and cost calculations
- Based on latest industry practices (2025)
- MLCommons ARES evaluation standards
- Anthropic, LangGraph, CrewAI best practices
- Production metrics (cost, latency, accuracy)
- Iterative System Design: See exactly how to build systems step-by-step in interviews
- Production Focus: Real costs, metrics, tradeoffs (not just toy examples)
- Complete Code: Every algorithm, system, and pattern has working code
- Modern Tech Stack: LangGraph, RAG, LoRA/QLoRA, agentic AI (2025 standards)
- Interview-Optimized: 45-minute format, STAR stories, questions to ask
This repo is perfect for:
- ML Engineers preparing for senior roles
- Software Engineers transitioning to ML/AI
- Data Scientists moving to ML engineering
- AI Researchers preparing for industry interviews
- Engineering Managers in ML/AI domains
- Start with Master Study Schedule
- Follow the 4-week complete preparation plan
- Work through notebooks in order
- Practice with coding problems
- Skim Interview Prep Complete Index
- Focus on weak areas (ML coding, system design, or LLM)
- Study iterative system design examples
- Practice with full mock interviews
- LLM Engineer: Focus on LLM production guide, RAG systems, fine-tuning
- ML Engineer: Focus on ML algorithms, production ML, MLOps
- ML Architect: Focus on system design, iterative examples
- Research Engineer: Focus on attention mechanisms, algorithms from scratch
This is a personal interview preparation repository made public to help others. Contributions are welcome!
Ways to contribute:
- Fix errors or improve explanations
- Add new examples or problems
- Share interview experiences
- Suggest additional topics
This repository is provided for educational purposes. Feel free to use, modify, and share with attribution.
This repository was created through iterative learning and preparation for ML/AI engineering roles. Special thanks to the ML/AI community for open-source resources and shared knowledge.
If you find this helpful or have suggestions, feel free to open an issue or start a discussion!
Star this repo if you find it helpful!
Good luck with your interviews!