Product Manager – Enterprise AI Operations & Observability

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

Product Manager – Enterprise AI Operations & Observability Department: Tech@Lilly

Location: Hyderabad, India

Position Type: Full-Time

Level: P4

Position Summary Eli Lilly is seeking a highly accomplished and hands on technology lead to drive our Enterprise AI Observability platform fuelling our ARE journey. This pivotal role is responsible for defining, implementing, and optimizing observability platform/s, and processes that ensure the reliability, performance, scalability, and security. The ideal candidate will bring deep expertise in driving of system of events end to end lifecycle management, self-serve instrumentation using agentic and automation, with a proven track in complex, large-scale environments. They will also partner closely with the ARE Architects and other platform engineers to execute a cohesive strategy that delivers measurable value across the organization, ensuring alignment between architectural vision and operational excellence. Key Responsibilities Strategic Leadership & Governance

• Execute and drive forward a comprehensive strategy for enterprise observability
• Establish governance frameworks, standards, and best practices for deployments.
• Ensure compliance with regulatory, security, and operational requirements.

AIOps

• Drive the adoption of AIOps practices for proactive issue detection, intelligent alerting, root cause analysis, and automated remediation.
• Establish and scale practices for secure, efficient, and reliable deployment, observability, and lifecycle management of platform.

Enterprise Observability

• Enhance and maintain 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.

Tooling, Platform Management & Automation

• 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.

Operational Resilience

• 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.

Performance & Reliability Optimization

• Utilize observability data to identify performance bottlenecks, capacity issues, and reliability risks.
• Work with relevant teams to implement improvements based on data-driven insights.
• Establish and execute performance strategy benchmarks utilizing baselines and KPIs.

Team Leadership & Enablement

• Build, mentor, and lead a high-performing team of engineers and specialists in AIOps
• Provide training and documentation to operational teams on leveraging observability platforms for troubleshooting and performance tuning.
• Foster a culture of innovation, continuous learning, and operational excellence.

Cross-Functional Collaboration

• Collaborate with engineering, data science, infrastructure, cybersecurity, and business teams to operationalize AI initia…