AIOps Engineer
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- Build SRE and observability foundations for AI-assisted operations
- Turn noisy telemetry into anomaly, root-cause and incident intelligence
- Design safe autonomous operations and prove measurable reliability value
Benchmark: Lam Research Observability & AIOps / Senior AI Observability + ServiceNow SRE & AIOps + enterprise zero-touch operations signals (current Sep 2026) Current bar: logs/metrics/traces/events and OpenTelemetry; SRE/SLO fluency; event correlation and noise reduction; anomaly detection and RCA; LLM/RAG operations knowledge; ITSM/runbooks; agentic incident response; safe remediation; hybrid cloud; Python/IaC; observability, governance and measurable MTTD/MTTR/toil improvements.
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Build SRE and observability foundations for AI-assisted operations
SRE, service health + observability foundations
Coverage: SLIs/SLOs / logs / metrics / traces / events / service maps / error budgets / incident signals
AIOps architecture + operational intelligence
Coverage: Telemetry pipelines / topology / ML for operations / prediction / correlation / remediation integration
Turn noisy telemetry into anomaly, root-cause and incident intelligence
Event correlation, anomaly detection + root-cause analysis
Coverage: Deduplication / suppression / anomaly models / topology context / probable cause / false positives
Incident automation, knowledge + agentic investigation
Coverage: ITSM / runbooks / RAG ops knowledge / incident summarization / triage / approvals / escalation
Design safe autonomous operations and prove measurable reliability value
Autonomous remediation + safe change/recovery
Coverage: Self-healing / policy gates / human approval / blast radius / rollback / change risk / recovery testing
AIOps platform governance, hybrid scale + value
Coverage: Hybrid cloud / model-agent governance / observability of AI / MTTD-MTTR / noise / toil / ROI
Role-Readiness Proof / Capstone
Build an AIOps layer for a realistic microservice or hybrid environment. Instrument services with OpenTelemetry and define SLOs; build a service/topology map; ingest logs, metrics, traces and events; create correlation, deduplication and anomaly rules/models; implement an incident assistant grounded in runbooks and prior tickets; integrate an ITSM workflow; automate one reversible remediation behind approval and policy gates; inject faults and run an incident drill; prove rollback; then publish dashboards for alert noise, MTTD, MTTR, toil, false positives and reliability impact.
Proof Rubric
- Show complete telemetry and actionable SLOs
- measurable signal/noise improvement
- reproducible anomaly and root-cause evidence
- credible incident and knowledge workflow
- safe automation boundaries and approvals
- explainable recommendations
- rollback and blast-radius controls
- ITSM/topology integration
- hybrid reliability
- measurable MTTD/MTTR/toil gains
- human oversight
- and a failure-learning loop that changes rules, models or runbooks