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Eli Lilly seeks an accomplished technology leader to drive the Enterprise AI Observability platform supporting the company's AI & Resilience Engineering (ARE) journey. This pivotal role is responsible for defining, implementing, and optimizing observability platforms and processes that ensure reliability, performance, scalability, and security across the organization.
Key Responsibilities:
Strategic Leadership & Governance: Execute a comprehensive enterprise observability strategy, establish governance frameworks and standards, and ensure compliance with regulatory, security, and operational requirements.
AIOps: Drive adoption of AIOps practices for proactive issue detection, intelligent alerting, root cause analysis, and automated remediation. Establish and scale practices for secure, efficient deployment and lifecycle management.
Enterprise Observability: Enhance observability strategy across infrastructure, applications, networks, security, and data systems. Standardize collection, correlation, and analysis of metrics, logs, and traces. 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. Optimize infrastructure for high availability and performance. Design intuitive dashboards and alerting systems. Champion automation using scripting, orchestration tools, and AI-driven solutions to reduce manual effort and enable self-healing systems.
Operational Resilience: Ensure high availability and resilience of mission-critical systems, especially AI/ML workloads. Collaborate with Service Management Office and production support teams. Enable methods to reduce mean time recovery (MTTR) and drive continuous improvements.
Performance & Reliability Optimization: Utilize observability data to identify performance bottlenecks, capacity issues, and reliability risks. Establish and execute performance strategy benchmarks using 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. 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 initiatives and ensure comprehensive observability coverage.
Budget & Vendor Management: Manage departmental budgets and vendor relationships to deliver cost-effective, scalable solutions.
Requirements:
Education: Bachelor's or master's degree in computer science, engineering, IT, or related field.
Experience: 15+ years of progressive technology experience, including 5–7 years in enterprise operations, ARE/SRE, and AI operations. Deep understanding of the lifecycle including development, deployment, observability, and retraining. Proven experience with enterprise observability across hybrid environments.
Technical Expertise: Expertise in AIOps principles and implementation. Proficiency with leading observability and MLOps tools and platforms (OpenTelemetry, Splunk, Datadog, Dynatrace, Prometheus, Grafana). Strong knowledge of cloud platforms, containerization, and microservices (Kubernetes, AWS, Azure, VMware, OpenShift). Experience with infrastructure-as-code and automation tools (Ansible, Terraform, Python, GitHub). Familiarity with ServiceNow, Jira, MCP, REST APIs.
Leadership & Soft Skills: Excellent leadership, communication, and stakeholder management skills. Demonstrated ability to build and lead high-performing engineering teams. Strong analytical and data-driven decision-making skills.
Preferred: Experience in regulated industries (healthcare, finance). Certifications in cloud platforms or operational frameworks (ITIL). Active participation in AIOps or MLOps professional communities.
