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Software Engineer Roadmap

You write code daily - backend, full stack, or frontend - and want to add AI to your toolkit

Heavy on LLMs, Agents, and production engineering. Light on deep math.

The route

  1. Week 1 - Python for ML: NumPy and pandas are the lingua franca of ML - essential for data manipulation
  2. Week 2 - Jupyter & Colab: Quick prototyping environment before writing production code
  3. Week 2 - Git & Version Control: Version control for ML projects - branching strategies and model tracking
  4. Week 2 - SQL for Data: Every AI feature reads and writes something - schema design, joins, and indexing for the data layer behind your model calls
  5. Week 2 - Foundations: Gives you the math vocabulary to read ML papers and understand model behavior
  6. Week 3 - Statistics: A/B testing, metrics interpretation, and data-driven decision making
  7. Week 3 - Probability Theory: Understand uncertainty in model predictions and confidence intervals
  8. Week 3 - Linear Algebra: Vectors and dot products - understand embeddings and similarity search in RAG systems
  9. Week 4 - Calculus: Understand gradients - how neural networks learn under the hood
  10. Week 4 - Optimization: Learning rates, optimizers, loss functions - the knobs you tune
  11. Week 5 - ML Algorithms: Common interview topic - know regression, trees, evaluation metrics
  12. Week 6 - Deep Learning: Neural networks, attention, transformers - the foundation of modern AI
  13. Week 7 - LLM: Core skill - prompt engineering, RAG, fine-tuning, and API integration, plus the streaming, token budgets, caching and rate limiting that decide whether the feature survives contact with real traffic
  14. Week 8 - Agentic AI & Multi-Agent Systems: Build autonomous AI agents with tool use, memory, and planning - both the orchestration backend and the multi-step interface a user can actually follow
  15. Week 9 - Software Engineering for AI: AI-specific software engineering - testing, architecture, prompt management, and the UX patterns non-deterministic systems need: loading states, partial output, and graceful failure
  16. Week 10 - MLOps & Deployment: Deploy models to production - monitoring, scaling, CI/CD for ML
  17. Week 11 - ML Pipeline: End-to-end ML workflows - data preprocessing to model serving
  18. Week 11 - AI Tools: AI coding tools are your biggest productivity multiplier - set up Claude Code and OpenClaw early
  19. Week 11 - AI on Cloud: Ship LLM features on AWS Bedrock, Vertex AI, or Azure OpenAI - the cloud is where your AI features actually run in production. A deep-dive shelf carries the serverless and managed-RAG services beyond that if you own the infrastructure
  20. Week 12 - Interview Q&A: Scenario-based interview prep - production problems that AI companies actually ask
  21. Week 12 - AI System Design: You already run classic system design. This is the same eight steps with four things changed: quality is fuzzy, cost is per token, the model is a dependency you do not own, and the worst failures return a normal-looking answer