Product Manager – Enterprise AI Operations & Observability at Eli Lilly and Company

Develop and execute a comprehensive strategy for enterprise AI operations and observability aligned with business and technology goals. Establish governance frameworks, standards, and best practices for AI/ML deployments and enterprise observability. Ensure compliance with regulatory, security, and operational requirements. Drive the adoption of AIOps practices for proactive issue detection, intelligent alerting, root cause analysis, and automated remediation. Establish and scale MLOps practices for secure, efficient, and reliable deployment, observability, and lifecycle management of AI/ML models. Define and implement a robust observability strategy across infrastructure, applications, networks, security, and data systems. Standardize the collection, correlation, and analysis of metrics, logs, and traces across all technology layers. Build predictive capabilities and dashboards to anticipate failures and enable proactive interventions. Treat observability as a product, continuously iterating to meet evolving business needs. Evaluate, implement, and manage advanced observability, and AIOps platforms and tools. Optimize and scale observability of infrastructure for high availability and performance. Design intuitive, high-value dashboards and alerting systems that clearly visualize system health and performance. Champion automation using scripting, orchestration tools, and AI-driven solutions to reduce manual effort and enable self-healing systems. Partner with automation teams to develop and implement automation scripts and workflows. Ensure high availability and resilience of mission-critical systems, especially AI/ML workloads. Collaborate closely with the Service Management Office and production support teams to drive impactful outcomes and elevate operational success. Enable methods to reduce mean time recovery (MTTR) and drive continuous operational improvements. Utilize observability data to identify performance bottlenecks, capacity issues, and reliability risks. Work with relevant teams to implement solutions for performance and reliability optimization.