The $500K+ Skill in 2025
AI Engineers are the highest-paid developers in tech right now. According to Levels.fyi, senior AI Engineers at top companies earn 700K+ TC. Why? Because companies are desperate for people who can actually ship AI products—not just experiment with ChatGPT.December 2025 Update: This course now covers GPT-4.5, Claude 3.5 Opus, Gemini 2.0 Flash, the OpenAI Responses API, and the latest agentic patterns including computer use and MCP integrations.
- A production RAG pipeline that handles 100K+ documents with hybrid search
- Multi-agent systems with LangGraph that automate complex workflows
- MCP servers that connect AI to databases, APIs, and external tools
- AI applications with proper error handling, caching, and observability
What makes this different? Every module includes production code you can deploy. No toy examples. No “hello world” chatbots.
What Companies Are Building Right Now
| Company Type | AI Applications | Your Skills |
|---|---|---|
| Startups | AI copilots, document automation | RAG, Agents, APIs |
| Enterprise | Knowledge bases, workflow automation | Vector DBs, Multi-agent |
| Dev Tools | Code assistants, MCP integrations | Tool use, LangGraph |
| SaaS | AI features, smart search | Embeddings, Caching |
Prerequisites (Crash Courses Included)
New to Python or backend development? We’ve got you covered:Python Crash Course
Types, async, classes, decorators—everything for AI work
FastAPI Crash Course
Build production APIs: routing, streaming, auth, deployment
Database & ORM
PostgreSQL, SQLAlchemy, pgvector, async patterns
What You’ll Build
Project 1: Smart Document Q&A
Production RAG system with hybrid search, re-ranking, and citations. Handles PDFs, docs, and web pages at scale.
Project 2: AI Code Reviewer
Agent that reviews PRs, suggests fixes, and explains changes. Uses function calling, structured outputs, and tool use.
Project 3: Research Assistant
Multi-agent system using LangGraph that researches topics, synthesizes information, and writes comprehensive reports.
Project 4: MCP Database Server
Build an MCP server that gives AI models access to your PostgreSQL database with read/write capabilities.
Project 5: DocuMind AI SaaS
Full-stack AI document assistant with multi-tenancy, usage tracking, and real-time streaming—your portfolio piece.
Bonus: Computer Use Agent
Agent that can control a browser/desktop to automate tasks using Anthropic’s computer use capabilities.
Course Modules
LLM Fundamentals
How LLMs actually work. Tokenization, embeddings, attention, inference costs.
Prompt Engineering
System prompts, few-shot learning, chain-of-thought, and prompt optimization.
OpenAI & APIs
Master function calling, structured outputs, streaming, vision, and cost optimization.
Vector Databases
pgvector, Pinecone, hybrid search, chunking strategies, and indexing.
RAG Systems
Build RAG that actually works. Query expansion, re-ranking, evaluation.
Fine-Tuning
When and how to fine-tune. OpenAI fine-tuning, LoRA, QLoRA patterns.
AI Agents
ReAct agents, tool use, memory systems, and autonomous workflows.
LangChain
Chains, prompts, memory, tools, and production patterns with LangChain.
LangGraph
Complex workflows, human-in-the-loop, parallel execution, state management.
MCP Protocol
Build MCP servers to connect AI to databases, APIs, and tools.
Agentic Architecture
Multi-agent architectures, supervisor patterns, and production design.
Evaluation & Testing
LLM-as-Judge, eval pipelines, CI/CD for AI, and monitoring.
Deployment & Scaling
Caching, rate limiting, model routing, and production infrastructure.
Capstone Project
Build DocuMind AI—a complete AI SaaS from scratch.
Learning Path
1
Week 0: Prerequisites (Optional)
For those new to Python/Backend
- Python crash course: async, types, classes
- FastAPI: APIs, streaming, dependency injection
- Databases: PostgreSQL, SQLAlchemy, migrations
2
Week 1-2: Foundations
Goal: Understand LLMs deeply, not superficially.
- How transformers and attention work
- Tokenization, context windows, and costs
- Embeddings and semantic similarity
- Prompt engineering that actually works
3
Week 3-4: APIs & Tool Use
Goal: Master the OpenAI API beyond basics.
- Streaming responses for real-time UX
- Function calling for structured actions
- Structured outputs with Pydantic
- Vision and multimodal inputs
4
Week 5-6: Vector Search & RAG
Goal: Build RAG systems that don’t suck.
- Chunking strategies that preserve context
- Hybrid search (semantic + keyword)
- Re-ranking for precision
- Evaluation and continuous improvement
5
Week 7-8: Agents & Production
Goal: Deploy AI systems that scale.
- Agent architectures and patterns
- LangGraph for complex workflows
- MCP for tool integration
- Caching, rate limiting, observability
Prerequisites
Python Intermediate
Classes, async/await, type hints, virtual environments. No ML experience needed.
Basic SQL
SELECT, JOIN, indexes. We’ll use PostgreSQL with pgvector.
REST APIs
HTTP methods, JSON, headers. We’ll build FastAPI services.
Command Line
Navigate directories, run scripts, use git. Docker is a plus.
Tech Stack (2025 Edition)
| Category | What You’ll Use |
|---|---|
| Languages | Python 3.12+, TypeScript (optional), SQL |
| LLM Providers | OpenAI (GPT-4.5, GPT-4o), Anthropic (Claude 3.5), Google (Gemini 2.0), Ollama (local) |
| Frameworks | LangChain 0.3+, LangGraph, FastAPI, Pydantic v2 |
| Vector DB | pgvector (PostgreSQL 16+), Pinecone, Chroma, Qdrant |
| Protocols | Model Context Protocol (MCP), OpenAI Responses API |
| Infrastructure | Docker, Redis, PostgreSQL, Supabase |
| Observability | LangSmith, Langfuse, OpenTelemetry |
Who Is This For?
Software Engineers
Software Engineers
You can code but haven’t built AI systems. You want to add AI features to products or transition into AI engineering. This course takes you from “I’ve used ChatGPT” to “I ship AI products.”
Backend Developers
Backend Developers
You build APIs and services. You want to add LLM capabilities—chatbots, document search, automation. You’ll learn to integrate AI while maintaining the reliability you’re used to.
Data Scientists
Data Scientists
You know ML but struggle with production deployment. Notebooks are great for experimentation, but you want to build real applications. This course bridges the gap.
Startup Founders
Startup Founders
You need to build AI features fast. You can’t afford to hire a team of specialists. This course gives you the skills to prototype and ship AI products yourself.
What You’ll Walk Away With
4 Portfolio Projects
Production-ready projects you can demo to employers or use in your products.
Reusable Code
Templates and patterns you can copy into any project.
Deep Understanding
Know why things work, not just how to copy-paste.
Start Here
Begin with LLM Fundamentals
Understand how large language models actually work before building with them