Lead Product Manager AIOPs

The AIOps team is responsible for modernizing IT operations through intelligent observability, event correlation, anomaly detection, predictive insights, and automation. They partner with AIOps vendors, IT Operations, infrastructure, platform engineering, SRE, service management, and application teams to reduce operational noise, improve service reliability, and accelerate incident response across a complex enterprise environment.

Responsibilities

• Execute the enterprise AIOps strategy, operating model, and adoption plan across the organization.
• Build scalable observability and automation capabilities and lead cross‑functional teams to improve reliability, operational efficiency, and service outcomes.
• Execute the enterprise AIOps roadmap, aligning delivery plans and priorities with reliability goals, operational maturity, and business outcomes.
• Lead cross‑functional collaboration across infrastructure, SRE, platform engineering, service management, and application teams to operationalize AIOps capabilities at scale.
• Establish governance, success metrics, and operating rhythms to measure improvements in alert quality, incident reduction, MTTD, MTTR, service health, and product adoption.
• Drive adoption of AIOps capabilities by aligning use cases, implementation priorities, and operational workflows with business needs, platform readiness, and measurable service outcomes.
• Drive integration and data ingestion of observability standards across logs, metrics, traces, events, and topology data to improve visibility across cloud, on‑premises, and hybrid environments.
• Implement AIOps use cases, including noise reduction, event deduplication, correlation, anomaly detection, root cause analysis, and predictive alerting.
• Partner with engineering teams to build automated remediation and self‑healing workflows integrated with observability and ITSM platforms.
• Evaluate, implement, and optimize AIOps and observability platforms, ensuring integration with monitoring tools, CMDB, incident workflows, knowledge systems, and automation frameworks.
• Present strategy, progress, and outcome metrics to senior leadership and influence decisions on tooling, process simplification, and operating model improvements.
• Build and mentor a high‑performing team, fostering technical depth in observability, data analysis, incident intelligence, and automation engineering.

Qualifications

• 7–10+ years of experience in IT operations, SRE, observability, platform engineering, or related disciplines, with demonstrated leadership in large‑scale enterprise environments.
• Strong understanding of AIOps concepts, including event correlation, anomaly detection, root cause analysis, noise reduction, predictive analytics, and automated remediation.
• Hands‑on experience with observability and monitoring ecosystems spanning logs, metrics, traces, topology, alerting, and service mapping.
• Experience integrating AIOps capabilities with incident management, ITSM, CMDB, and automation platforms to improve operational workflows.
• Proven ability to lead teams, influence stakeholders, manage vendor relationships, and translate technical capabilities into measurable business value.
• Strong communication, analytical thinking, and executive presentation skills.
• Demonstrated ability to identify, prioritize, and deliver AIOps use cases that improve signal quality, incident response, operational efficiency, and service reliability.
• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field.

Preferred Qualifications

• Experience with enterprise observability and AIOps platforms, including ServiceNow ITOM, Splunk, Dynatrace, Datadog, Moogsoft, BigPanda, New Relic, and similar technologies.
• Familiarity with cloud‑native platforms, Kubernetes, distributed systems, and modern telemetry standards such as OpenTelemetry.
• Experience defining KPIs, SLOs, and operational maturity frameworks for enterprise reliability and service performance.
• Knowledge of machine learning applications in operations, including forecasting, anomaly detection, pattern recognition, and recommendation models.
• Experience maturing enterprise AIOps operating models, including use case rollout, adoption measurement, workflow integration, and continuous improvement of operational outcomes.
• Relevant certifications in cloud, ITSM, observability, or platform engineering are a plus.

Benefits Health