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¶ Linux

Shell Scripting

Python for DevOps

Git

AI for DevOps

Docker

Kubernetes 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 current 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¶ Beginner

Linux Administrator

Cloud Engineer

DevOps Engineer Estimated duration¶ 10–14 weeks of focused study (tutorials + labs). Stretch if you are new to Linux or cloud.

📌 AI for DevOps (Mulshi)
🏢 rebash
📍 Mulshi