AI Infrastructure Engineer Learning Track

This repository provides a comprehensive, production-ready learning path for individuals aiming to become AI Infrastructure Engineers. It is designed for those with 2-4 years of experience, offering practical skills to build, deploy, and maintain scalable ML infrastructure, directly preparing them for high-demand, well-compensated roles in the field.

How It Works

The curriculum adopts a hands-on methodology, featuring production-grade code stubs with educational TODO comments. It guides learners through building ML infrastructure from scratch using Docker and Kubernetes, deploying production ML systems with auto-scaling and robust monitoring, implementing end-to-end MLOps pipelines with tools like Airflow, MLflow, and DVC, and deploying cutting-edge LLM infrastructure leveraging vLLM, RAG, and vector databases. The approach emphasizes real-world patterns employed by leading tech companies and utilizes a modern technology stack.

Quick Start & Requirements

To begin, clone the repository, create and activate a Python 3.11 virtual environment, and install dependencies using pip install -r requirements.txt. Prerequisites include intermediate proficiency in Python 3.9+, Linux/Unix command line, Git fundamentals, basic ML concepts (PyTorch/TensorFlow), Docker, and an introduction to Kubernetes. Completing the associated Junior AI Infrastructure Engineer curriculum is recommended. The learning path comprises over 500 hours of content. Cloud costs can be managed within free tiers for most learning activities, with optional GPU costs for advanced projects. Key resources include the GETTING_STARTED.md guide, Prerequisites documentation, and GitHub Discussions.

Highlighted Details

• Features 10 complete learning modules and 3 production-grade projects, covering foundational concepts through advanced LLM infrastructure.
• Emphasizes modern technologies such as vLLM, RAG, vector databases, and GPU optimization for LLM deployment.
• Provides practical guidance on cloud cost optimization, with potential savings of 60-80%.
• Directly maps learning outcomes to AI Infrastructure Engineer roles, with associated salaries ranging from $120,000 to $180,000.
• Includes comprehensive quizzes for modules and detailed technology version specifications.

Maintenance & Community

This project is maintained by the AI Infrastructure Curriculum Project. Community support and discussions are available via GitHub Discussions and the Issues tracker. Direct contact can be made at ai-infra-curriculum@joshua-ferguson.com.

Licensing & Compatibility

The project is licensed under the MIT License, a permissive open-source license that allows for broad compatibility with commercial use and integration into closed-source projects.

Limitations & Caveats

As of the May 2026 updates, the repository's exercises are still under active development, with 32 out of 119 promised exercises completed. Some structural cleanup has occurred, and solutions for exercises are now referenced in a separate repository (ai-infra-engineer-solutions).