AI Engineering Curriculum - From Fundamentals to Enterprise Scale

Master AI Engineering

From Fundamentals to Enterprise Scale

"AI Engineering has levels to it" - A comprehensive curriculum that takes you from using AI APIs to building enterprise-scale systems

Originally inspired by Zach Wilson (@eczachly)'s X post on AI Engineering levels
Based on "The AI Engineering Continuum: A White Paper on the Levels of System Mastery" and industry best practices

The Four Levels of AI Engineering

Progressive mastery from fundamentals to scale

Level 1

Using AI

Master the fundamentals of AI interaction

Prompt Engineering (Zero-shot, Few-shot, CoT)
API Integration (OpenAI, Anthropic, Cohere)
Token Management & Context Windows
Cost Optimization Strategies

Explore Level 1 →

Level 2

Integrating AI

Build intelligent AI-powered systems

Retrieval Augmented Generation (RAG)
Vector Databases & Embeddings
Agent Architecture & Tool Use
Caching & Performance Optimization

Explore Level 2 →

Level 3

Engineering AI Systems

Transform prototypes into production-ready systems

Fine-tuning (LoRA, QLoRA, RLHF)
Safety Guardrails & Compliance
Multi-Model Architectures
Evaluation Frameworks

Explore Level 3 →

Level 4

Optimizing AI at Scale

Deploy and operate AI at enterprise scale

Distributed Inference (vLLM, Ray)
Context & Memory Management
Cost vs Performance Trade-offs
Privacy, Compliance & Governance

Explore Level 4 →

Why This Curriculum?

Built for the modern AI engineering landscape

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Production-Focused

Learn to build systems that work in the real world, not just in notebooks

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Current & Updated

Based on 2024-2025 state-of-the-art practices and technologies

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Industry-Aligned

Skills that directly transfer to AI engineering roles

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Progressive Learning

Structured path from beginner to advanced practitioner