Senior Manager-Digital Product Management

Role focus: Lead product strategy and execution for AI-powered operations capabilities that move Technology Operations from reactive incident response toward AI-assisted, AI-augmented, and safely automated operations.

As a Senior Manager, Digital Product Management for AI Ops, you will lead product strategy and execution for a portfolio of AI-powered operations capabilities that help transform Technology Operations from manual, reactive incident response to AI-assisted, AI-augmented, and increasingly autonomous operations. You will define and manage product roadmaps across agentic root-cause analysis, event correlation and noise reduction, operational copilots, knowledge and skills management, guided remediation, and safe self-healing workflows.

This role sits at the intersection of product management, SRE, observability, ITSM, automation, data platforms, and responsible AI. You will translate operational pain points into AI-enabled product capabilities, define success metrics, prioritize the backlog, and partner closely with engineering, SRE, application support, infrastructure, risk, compliance, and vendor partners to deliver production-grade AI Ops products at enterprise scale.

The ideal candidate is a product leader who can combine strong customer discovery and roadmap discipline with the technical fluency to reason about AI/ML systems, GenAI and agentic workflows, observability data, knowledge quality, model evaluation, human-in-the-loop controls, and governed automation in a regulated environment.

• Own the product vision, roadmap, backlog, and success metrics for AI Ops products across incident detection, triage, RCA, proactive risk detection, guided remediation, and self-healing automation.

• Translate customer and operational needs from SRE, Application Support, Mission Control, infrastructure, and platform teams into product requirements, user journeys, acceptance criteria, and measurable outcomes.

• Define AI-enabled workflows that use observability data, ITSM data, knowledge sources, dependency maps, automation catalogs, and operational feedback to improve detection, diagnosis, remediation, and post-incident learning.

• Partner with engineering and architecture teams to shape agentic product capabilities, including agent orchestration, skills, tools, prompts, knowledge retrieval, memory, evaluation, feedback loops, and governance controls.

• Prioritize features based on customer value, operational impact, risk reduction, adoption, cost-to-serve, and alignment to the broader AI Ops / Zero Ops roadmap.

• Drive adoption of AI Ops capabilities through enablement plans, self-service onboarding journeys, product documentation, stakeholder communications, demos, feedback channels, and value realization tracking.

• Establish and track product KPIs such as MTTR, MTTD, alert-noise reduction, incident-volume reduction, manual-effort reduction, AI-assisted resolution rate, user adoption, recommendation helpfulness, and automation execution.

• Partner with risk, security, privacy, compliance, and governance stakeholders to ensure AI Ops capabilities are auditable, explainable, permissioned, human-governed where appropriate, and safe for production use.

• Evaluate internal and external AIOps, observability, automation, and agentic AI solutions; inform buy/build/partner decisions and ensure vendor capabilities integrate into the enterprise AI Ops ecosystem.

• Lead cross-functional planning and operating routines across Product, Engineering, SRE, Application Support, Infrastructure, Program Management, and vendor partners to deliver AI Ops capabilities from concept through production adoption.

Key Qualifications

• Bachelor's degree in Information Systems, Computer Science, Information Technology, Engineering, Business, or comparable experience; advanced degree preferred.

• 8+ years of successful product management, engineering, deployment, or operations experience in enterprise-grade technology environments.

• Experience product-managing AI, ML, GenAI, automation, observability, IT operations, SRE, or platform products.

• Strong understanding of incident management, problem management, change management, ITSM workflows, and production operations.

• Familiarity with AIOps capabilities such as event correlation, anomaly detection, root-cause analysis, predictive operations, automated remediation, and self-healing.

• Demonstrated ability to translate ambiguous operational problems into product strategy, roadmaps, user stories, requirements, and measurable outcomes.

• Experience defining success metrics for AI or data-driven products, including adoption, quality, accuracy/he