AI for DevOps Engineer¶
Duration: 10–14 weeks · Difficulty: intermediate · Badge: AI
Practical AI for automation — APIs, agents, and DevOps tooling on a solid Python base.
Complete roadmap¶
Target audience¶
Engineers who want the AI for DevOps Engineer skill profile and job outcomes below.
Job roles¶
- AI for DevOps Engineer
- Automation Engineer
- MLOps Associate
Expected salary ranges¶
Emerging automation / AI-ops roles — treat as directional guidance only.
Prerequisites¶
- Comfort with a laptop and a terminal
- Complete earlier phases before later ones when marked ready
- Prefer the Getting Started overview if you are new to the academy
Phases¶
Prerequisites¶
- Linux — ready · 25 tutorials
- Shell Scripting — ready · 18 tutorials
- Python for DevOps — ready · 27 tutorials
- Git — ready · 20 tutorials
AI¶
- AI for DevOps — stub / coming soon
Platform context¶
- Docker — ready · 20 tutorials
- Kubernetes — ready · 20 tutorials
Skills gained¶
- Ordered mastery of the technologies on this path
- Hands-on labs and production-oriented habits
- Interview and certification readiness for mapped exams
Projects¶
See the Projects catalog and Capstones. Map picks to this path’s technologies.
Capstone¶
Choose a capstone that exercises the final phases of this path (for example Status API for DevOps / Kubernetes, or the Python automation framework for AI for DevOps).
Interview roadmap¶
- Finish ready technology tracks on this path
- Use Interview Guides per technology
- Rehearse troubleshooting stories from labs
Certification roadmap¶
See Certifications
Related career paths¶
Estimated duration¶
10–14 weeks of focused study (tutorials + labs). Stretch if you are new to Linux or cloud.