Product Manager – Enterprise AI Operations & Observability

Define, execute, and own the comprehensive product roadmap for the Enterprise AI Observability platform supporting Lilly's Automated Reliability Engineering (ARE) journey.

Drive the enterprise-wide adoption of AIOps practices for proactive anomaly detection, intelligent alerting, automated Root Cause Analysis (RCA), and self-healing remediation.

Treat observability as an internal product, standardizing telemetry collection, correlation, and analysis (metrics, logs, traces) across infrastructure, microservices, cloud networks, and data systems.

Lead the implementation of self-serve instrumentation using agentic AI, Model Context Protocol (MCP), and automation workflows to minimize manual operational overhead.

Partner with ARE Architects, Software Product Engineering (SPE), cybersecurity, and platform engineering teams to maintain high availability and operational resilience for mission-critical AI/ML workloads.

Build predictive health dashboards, define SLIs/SLOs, and systematically reduce Mean Time to Recovery (MTTR) across hybrid enterprise platforms.

Manage departmental platform budgets, tool vendor relationships, licensing contracts, and cost-effective scaling strategies.

Recruit, mentor, and guide a high-performing engineering team specializing in AIOps, telemetry instrumentation, and platform operations.

Comprehensive mastery of enterprise observability frameworks, SRE/ARE paradigms, and end-to-end AI/ML model operational lifecycles.

In-depth hands-on knowledge of observability and telemetry tools: OpenTelemetry, Splunk, Datadog, Dynatrace, Prometheus, and Grafana (PLGJ stack).

Strong architectural command over hybrid enterprise infrastructure: Kubernetes, AWS, Azure, VMware, and Red Hat OpenShift.

Advanced proficiency in infrastructure automation and runbook engineering using Python, Terraform, Ansible, GitHub Actions, REST APIs, and MCP.

Knowledge of IT service management integration via ServiceNow and agile product delivery governance using Jira.

Serve as the senior Product Management authority (P4 level) establishing observability governance, instrumentation standards, and operational best practices.

Influence cross-functional senior leadership across data science, digital product engineering, and operations through data-driven reliability metrics.

Cultivate a product-led culture emphasizing continuous reliability engineering, automated toil reduction, and operational excellence in regulated environments.

Executive-tier total compensation package including competitive base salary, annual performance incentives, and long-term equity/stock units.

Comprehensive family healthcare, life insurance, retirement pension programs, and wellness allowances.

Extensive leadership development resources and global internal mobility pathways across Tech@Lilly digital hubs.