Engineering Product Manager - Observability AI

Cisco is revolutionizing how data and infrastructure connect and protect organizations in the AI era. In this role, you will be responsible for building AI-driven observability products, managing the product lifecycle, and ensuring alignment with business outcomes and customer needs in the observability domain.

Responsibilities:

• Take end-to-end ownership of multiple observability products within a portfolio, driving innovation in AI-powered monitoring, troubleshooting, remediation, and incident response
• Shape the product portfolio strategy over a 12-18 month horizon, with clear milestones focused on enhancing observability capabilities such as AI-driven root cause analysis, anomaly detection, alert correlation, and remediation
• Develop multi-quarter product roadmaps that align market needs, AI observability trends, AI evaluation quality standards, and business strategy
• Serve as a product and domain expert with deep technical knowledge of modern workloads, observability telemetry, AI-driven analytics, and AI evaluation methodologies including groundedness, hallucination detection, task success, and agent collaboration
• Provide input into competitive analysis and market validation for AI observability
• Solicit and incorporate customer feedback from diverse channels to continuously improve observability features, AI evaluation quality, and user experience
• Create alignment between product teams, stakeholders, and leadership to strengthen AI observability product lines and drive adoption
• Act as a product spokesperson internally and externally, evangelizing observability innovations, and benefits

Requirements:

• Bachelor's degree plus 8 years of related experience, or Master's degree plus 6 years, or PhD plus 3 years in product management or related fields
• Experience with AI technologies, observability platforms, monitoring solutions, and AI evaluation frameworks is highly desirable
• Expertise in building AI agentic systems, observability data analytics, and AI evaluation metrics such as groundedness, reliability, task completion, and agent teamwork
• Familiarity with AI security, compliance, telemetry standards, and AI quality management tools
• Strong cross-functional teamwork skills with engineering, data science, and customer success teams