What DevOps Engineers Should Learn Next | The AI Skills Gap | Brillius

Career guidance for DevOps engineers. The specific AI and AIOps skills most in demand in 2025 and the fastest path to building them with hands-on practice.

What skills should DevOps engineers learn next?

The highest-value next skills for DevOps engineers are AI-powered observability, automated incident response, ML-based anomaly detection, and AI-assisted runbook automation — collectively known as AIOps.

Is AI and AIOps the next step after traditional DevOps?

For engineers with solid DevOps foundations, yes. AIOps is the natural progression for those targeting senior SRE, platform engineering, and AI-augmented operations roles — the fastest-growing segment of the DevOps job market.

What specific tools should DevOps engineers add to their skill set?

DevOps engineers should add AI observability platforms like Dynatrace and Datadog AI features, AIOps event correlation tools like BigPanda and Moogsoft, and hands-on experience with ML-assisted deployment and incident management workflows.

How do DevOps engineers stay relevant as AI transforms operations?

DevOps engineers stay relevant by building AI-tool literacy, practicing AIOps workflows in hands-on environments, and demonstrating measurable operational outcomes with AI-augmented tooling — not by waiting for AI adoption to reach them.

What is the fastest way to learn AIOps as a working DevOps engineer?

Structured hands-on training with a guided curriculum and live lab environments. Brillius AI Labs compresses the learning curve by pairing structured lessons with immediate practice, AI instructor support, and role-specific interview preparation.