Curriculum Overview

Overview

Designed for experienced engineers and technical leaders (average student: 12-15 years in industry). Raw Python first (no hiding behind frameworks), then frameworks, then production deployment on AWS.

Returning to code? A Python refresher module is included, covering exactly the Python you need for AI work.

Step 1: First Principles (Weeks 1-6)

• Week 1: LLM fundamentals: your first API calls, system vs user prompts, conversation history, chat vs reasoning models.
• Week 2: Building AI apps: your first UI, simple RAG, guardrails, dynamic context injection, tokens, costs and prompt caching.
• Week 3: Tool calling: single, multiple and consecutive tool calls, plus push notifications for your AI.
• Week 4: Retrieval-Augmented Generation done properly: embeddings, chunking, vector databases, retrieval and the full RAG pipeline.
• Week 5: Your first deployment: preparing an app for the cloud, secrets, logs, debugging in production. Project 1 ships: your Digital Twin, a deployed RAG chatbot of yourself you can demo in any interview.
• Week 6: Agentic AI foundations: the agentic loop, agentic prompt engineering, AI evals and LLM-as-judge.

Step 2: Advanced AI Architectures (Weeks 7-9)

• Week 7: Multi-agent orchestration: an orchestrator commanding specialized agents (research, writing, image generation), handoffs, traces and debugging. Project 2: Researcher-Writer multi-agent system with production guardrails.
• Week 8: Model Context Protocol: building and running MCP servers, the three server types, security risks, one agent with multiple servers. Project 3: Multi-MCP Agent.
• Week 9: Frameworks and advanced patterns, now that you know what's under the hood: LangChain, LangGraph, Agents SDK, multi-agent design patterns.

Step 3: Production AI on AWS (Weeks 10-12)

• Week 10: Ship it: your first live AI product. Instant deployment on Vercel, full-stack architecture (Next.js + FastAPI), real-time streaming, user authentication, subscription billing, API cost management. Project 4: end-to-end app deployment on Vercel.
• Week 11: AWS foundations: secure IAM setup, cost monitoring, Docker and ECR, App Runner with auto-scaling, serverless with S3, Lambda and API Gateway, CloudFront, Amazon Bedrock, CloudWatch. Project 5: AWS deployment (serverless AI agent).
• Week 12: Production engineering: Infrastructure as Code with Terraform, dev/test/prod environments, CI/CD with GitHub Actions, observability (CloudWatch dashboards, Langfuse, LLM-as-judge), guardrails, prompt injection defenses, cost control. Project 6: advanced AWS deployment (fully automated, monitored and secured CI/CD pipeline).

After the Core Curriculum

• CAIP Exam. AWS Certified AI Practitioner: prepare for and sit the certification exam with the program's support.
• Project 7: your custom capstone. Scoped with your coach for your industry and target role, built end-to-end by you, reviewed to a professional standard.
• Industry fellowship. Real consulting work for a real business, supervised by your coaches. Production experience you can put on your CV.
• Weeks 13-26. Full support to your goal: personal branding, unlimited mock interviews (technical and behavioral), portfolio polish, office hours 4x per week, and unlimited coaching until you land the role.

Running Alongside, From Week 1

Personalized roadmap and success manager. CV + LinkedIn review with Tony (12 years technical recruiting at Google) in your first week. Live office hours 4x per week, all Eastern time: Tue 7:30pm LLMs & Agentic AI · Wed 8:00pm Personal Branding · Thu 7:00pm AWS Production Deployment · Sat 11:00am LLMs & Agentic AI. Unlimited coaching calls, unlimited technical and behavioral mock interviews, portfolio and GitHub revamp.