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A comprehensive, hands-on learning path for AI Infrastructure Engineers at all levels - from entry-level to principal roles.

This curriculum provides production-ready training for AI Infrastructure Engineers, covering everything from foundational Python and Kubernetes to advanced distributed training, LLM infrastructure, and enterprise architecture. Each track includes hands-on exercises, real-world projects, and complete solution implementations.

Total Content:

• 📚 12 Learning Tracks (Junior → Principal levels)
• ✅ 12 Solutions Repositories (Complete implementations)
• 🎓 500+ Hands-On Exercises
• 🚀 50+ Real-World Projects
• ⏱️ 2,500+ Hours of learning material

Recently Added Documentation:

• 📋 Technology Versions Guide - Comprehensive version specifications for 100+ tools and frameworks
• 🗺️ Curriculum Cross-Reference - Complete mapping between Junior and Engineer tracks showing skill progression and learning paths
• 📈 Career Progression Guide - Detailed career ladder from L3 (Junior) to L8 (Principal Architect) with compensation ranges and timelines
• 📝 New Quizzes - 265+ quiz questions added to Engineer track (modules 102-110)
• 🎯 New Exercises - LLM basics, GPU fundamentals, Terraform/IaC, and Airflow workflow exercises in Junior track

Entry Level (0-2 years)
    ↓
Junior Engineer → Engineer
    ↓
Intermediate (2-4 years)
    ↓
┌─────────────────────┬──────────────────────┬─────────────────────────┐
│                     │                      │                         │
MLOps Engineer        ML Platform Engineer   Performance Engineer      Security Engineer
│                     │                      │                         │
└─────────────────────┴──────────────────────┴─────────────────────────┘
    ↓
Advanced (4-6 years)
    ↓
Senior Engineer ────────────→ Architect
    ↓                             ↓
Leadership (6-8 years)      Advanced Arch (8-10 years)
    ↓                             ↓
Team Lead ───────────────→ Senior Architect
    ↓                             ↓
Principal Level (8-15+ years)
    ↓                             ↓
Principal Engineer ──────→ Principal Architect

Select based on your experience level and career goals.

# Example: Junior Engineer track
git clone https://github.com/ai-infra-curriculum/ai-infra-junior-engineer-learning.git
cd ai-infra-junior-engineer-learning

# Read the curriculum
cat README.md

# Start with Module 001
cd lessons/mod-001-python-fundamentals
cat README.md

Work through hands-on exercises in each module.

Compare your work with the solutions repository.

Languages: Python, Bash, HCL (Terraform), YAML ML Frameworks: PyTorch, TensorFlow, Scikit-learn Orchestration: Kubernetes, Helm, ArgoCD, FluxCD Cloud: AWS, GCP, Azure (multi-cloud) Containers: Docker, containerd MLOps: MLflow, Kubeflow, DVC, Feast Monitoring: Prometheus, Grafana, Loki, Jaeger IaC: Terraform, Pulumi CI/CD: GitHub Actions, GitLab CI LLMs: vLLM, Llama, Mistral, RAG systems GPU: CUDA, NCCL, TensorRT

By completing this curriculum, you will be able to:

✅ Build production ML infrastructure from scratch ✅ Deploy and optimize models at scale (1000s of models) ✅ Manage GPU clusters efficiently (85%+ utilization) ✅ Reduce costs by 30-50% through optimization ✅ Implement MLOps pipelines with CI/CD ✅ Design multi-cloud architectures ✅ Lead technical teams and initiatives ✅ Define technical strategy for organizations

• Software engineers → ML infrastructure
• Data scientists → Infrastructure skills
• DevOps/SRE → ML specialization

• Junior engineers → Senior roles
• Mid-level engineers → Principal positions
• Engineers → Architecture tracks
• Individual contributors → Leadership

• Building ML infrastructure teams
• Training internal engineers
• Bootcamps & educational institutions

• Real-world scenarios from leading tech companies
• Metrics-driven success criteria
• Complete, tested implementations
• Best practices and anti-patterns

• 500+ hands-on exercises
• 50+ real-world projects
• Complete solution implementations
• Step-by-step guides

• Start with fundamentals
• Build to production systems
• Scale to enterprise architecture
• 28-44 hours per advanced exercise

• Active community
• Regular updates
• Modern tooling (2024-2025 versions)
• Industry-validated content

We welcome contributions! See CONTRIBUTING.md for guidelines.

Ways to contribute:

• Fix bugs in exercises or solutions
• Add new exercises or projects
• Improve documentation
• Share your learning experience
• Report issues or suggest improvements

This curriculum is licensed under the MIT License.

• Issues: Report bugs or request features via GitHub Issues
• Discussions: Ask questions in GitHub Discussions
• Community: Join our community channels

Current Status (October 2025):

• ✅ Junior Engineer track (complete)
• ✅ Engineer track (complete - 26/26 exercises)
• ✅ All 24 repositories created
• 🚧 Solutions being populated across tracks
• 🚧 Advanced tracks content in development

Coming in 2026:

• Video walkthroughs for key exercises
• Interactive labs and sandboxes
• Community projects and challenges
• Certification programs
• Live mentorship sessions

• Reduce infrastructure costs by 30-50%
• Improve GPU utilization from 40% to 85%+
• Cut model deployment time from days to hours
• Scale to 1000s of models in production

• Based on production scenarios from leading tech companies
• Reviewed by senior ML infrastructure engineers
• Updated with latest tools and best practices
• Aligned with real job requirements

• Clear progression path from Junior to Principal
• Multiple specialization tracks
• Leadership development included
• Portfolio-ready projects

Start your AI Infrastructure Engineering journey today! 🚀

Choose Your Track | Quick Start | Contributing

Maintained by: AI Infrastructure Curriculum Project Last Updated: October 2025 Total Repositories: 24 (12 learning + 12 solutions)