Principal AIOps Engineer

Position Summary

We are seeking a Principal AIOps Engineer with deep reliability and operations experience to build and scale intelligent operations across the enterprise. This role focuses on modernizing IT operations through observability, event intelligence, machine learning, and agentic AI—reducing alert noise, accelerating triage, and enabling closed‑loop automation.

• Lead the AIOps strategy, roadmap, and operating model (intake, triage, automation lifecycle, KPIs) to measurably improve MTTR, alert quality, and operational efficiency.
• Own the observability‑to‑AIOps pipeline (metrics, logs, traces, events) and drive standardization of telemetry, service health models, and actionable alerting across teams and platforms.
• Design and implement event intelligence: correlation, deduplication, suppression, anomaly detection, incident clustering, and probable‑cause analysis using topology/CMDB context.
• Advise operations, service owners, and leadership stakeholders; lead change enablement, adoption, and value measurement for AIOps and agentic automation across the organization.
• Develop Service Now‑centric AIOps integrations (ITSM + ITOM/Event Management where applicable): event ingestion, alert‑to‑incident policies, enrichment, assignment/routing, approvals, change workflows, and closure updates for auditable closed‑loop ops.
• Establish governance for operational AI (risk controls, approvals, auditability, data access, prompt/response logging, evaluation, and continuous improvement) in partnership with security, compliance, and operations.
• Build and ope rationalise agentic AI workflows for incident triage and resolution: signal summarisation, similar‑incident retrieval, knowledge article drafting, ticket updates, stakeholder communications, and human‑in‑the‑loop remediation.
• Enable closed‑loop automation and self‑healing by connecting AIOps detections to orchestrated actions (runbooks/workflows), with clear approvals, safety checks, and rollback paths.
• Partner with NOC/SOC, infrastructure, and application owners to onboard services into AIOps, define service models, and improve signal quality, escalation paths, and operational readiness.
• Create enablement materials (playbooks, operating procedures, dashboards) and coach teams on AIOps practices, agentic AI usage, and responsible automation.

• 10+ years of experience in SRE, production operations supporting highly available services along with experience with Product model.
• Proven technical leadership: ability to set direction, lead cross‑team initiatives, and advise stakeholders through architecture reviews, trade‑offs, and operational readiness.
• Strong programming/scripting skills (Python preferred) and experience building automation, integrations, and APIs.
• Experience integrating observability platforms and event sources across hybrid environments (cloud/on‑prem) and operating production‑grade monitoring/event management at scale.
• Strong Service Now experience as an ITSM system of record (Incident/Problem/Change; CMDB/asset concepts).
• Ability to build and operate integrations at scale (REST, webhooks, event management) to support automation and auditability.
• Automation & Integration Engineering:
  Python for automation and data/ML pipelines; experience building integrations, services, and operational tooling.
• Workflow orchestration and integrations (Service Now APIs, event pipelines, runbook automation) with strong reliability, security, and auditability practices.
• AIOps, ITSM/ITOM (Service Now) & Agentic AI Ecosystem:
  Observability:
  Prometheus/Grafana, Open Telemetry, ELK/Splunk/Datadog (or equivalent);
  Service Now ITSM/ITOM:
  Incident/Problem/Change, CMDB/service mapping concepts, and Event Management/AIOps integrations (where applicable);
  Agentic AI frameworks: building tool‑using agents, retrieval workflows, prompt/response logging, evaluation & guardrails;
  Operational ML/Analytics: anomaly detection and time‑series analysis, correlation approaches, and model/agent evaluation & monitoring in production.

• Demonstrated experience applying machine learning and/or LLM‑based approaches to operational…