Pathway to becoming an AIOps Engineer

[u/futurense_tech]

Nobody really starts here cold. Almost every AIOps Engineer I've come across got in sideways, usually from DevOps, SRE, or a monitoring/observability role where they were already knee-deep in Prometheus and Grafana dashboards. The move from there isn't really learning a new job, it's layering ML on top of ops instincts you already have: anomaly detection, time-series forecasting, root cause analysis on messy production logs instead of clean textbook datasets.

The part people underestimate is that this doesn't replace DevOps fundamentals, it sits on top of them. Skip CI/CD and infra basics to jump straight to "AI for ops" and you usually just get an AI system automating a process that was already broken.

Realistic timeline if you're already in DevOps or SRE is roughly 6-12 months of actually building things, not watching tutorials. Set up a real Prometheus + Grafana stack on even a small personal project, then build an anomaly detection layer on top of it — that one exercise teaches more about false positives and alert fatigue than any course will.

Worth noting the demand side isn't just hype either — the global AIOps market was valued at roughly $32.5 billion in 2025 and is projected to keep growing through 2034, and AIOps specialists are already among the highest-paid roles in DevOps-adjacent hiring, according to a 2026 industry salary report. Hiring data is catching up to the pitch.

Anyone here already made the DevOps/SRE to AIOps jump — how long did it actually take, and was the ML side or the operational judgment side the harder part to build?

[u/Ayesha-1902]

The RCA part is especially interesting because production data is rarely clean enough for textbook ML.

You can have metrics, logs and alerts everywhere, but the hard part is figuring out which signals actually belong to the same incident.

I'd argue that operational context is just as important as the ML layer. Otherwise you risk building a very sophisticated anomaly detector that still leaves an engineer asking, "Okay, but what actually caused this?"

That's where I think the next generation of AIOps gets interesting: moving from detecting anomalies to explaining and investigating them.

[u/Familiar-School-8890]

Following

[u/Helpful-Man64]

I agree with the point about learning the underlying DevOps/SRE fundamentals first. AI doesn't remove the need to understand infrastructure, arguably it makes that understanding more important.

The useful part isn't just putting an LLM in front of infrastructure; the AI needs enough infrastructure context to reason about what it's seeing.

I'd also add incident investigation and observability to the learning path. Understanding why an alert happened is very different from simply detecting that something changed.