AI Engineer — Role-Based Learning Hub

This top-level folder is a modular collection of role-specific, project-driven curriculum tracks. Each sub-folder targets a distinct job description and stands alone as a complete learning path.

Available Tracks

Track	Status	Focus	Estimated Duration
`cv-engineer/`	✅ Active	Computer Vision, Deep Learning, MLOps	20 weeks
`llm-engineer/`	🔜 Planned	LLMs, RAG, fine-tuning, agents	—
`mlops-platform/`	🔜 Planned	Kubeflow, SageMaker, infrastructure	—
`robotics-ai/`	🔜 Planned	ROS, SLAM, RL for robotics	—

How to Use This Hub

Pick the track that matches your target role.
Open that track's README.md for the full roadmap and weekly schedule.
Work through phases sequentially — each phase gates the next.
Use the interview-prep/ and system-design/ folders as running references throughout.

Cross-Track Skills (Shared Foundations)

Regardless of which track you pursue, these skills underpin every role:

Python proficiency — see cv-engineer/phase-00-foundations/
System design thinking — scalability, fault tolerance, distributed architecture
Cloud literacy — AWS / GCP / Azure fundamentals
Git & code review culture — PRs, CI/CD, linting, testing
Communication — writing technical specs, presenting to non-technical stakeholders

Adding a New Track

Create AI-Engineer/<role-name>/
Add a README.md with: overview, job description, prerequisites, phase structure, weekly schedule.
Add this track to the table above.
Mirror the lab structure from an existing track for consistency.

Philosophy: Every lab in this hub produces a real artifact — a working model, a deployed endpoint, a benchmarked system — not just filled-in notebooks. By the end of each track, you should have a GitHub portfolio that speaks louder than any resume line.