Senior Lead Site Reliability Engineer, Manager AI/ML and Data Platforms

What you'll do

• Define non-functional requirements and availability targets for large-scale data platforms and AI/ML workloads.
• Create high-quality designs, roadmaps, and program charters for distributed systems initiatives.
• Implement observability and reliability designs to ensure stable performance without increasing technical debt.
• Integrate enterprise-authorized AI capabilities into reliability workflows to accelerate design and operational decision-making.
• Establish team practices for safe, secure, and auditable AI usage in production operations.
• Debug and evolve critical components by analyzing application and infrastructure interdependencies.
• Provide tools and guidance to support scalable data platform infrastructure and engineering best practices.
• Contribute to the firm's site reliability community through internal forums and technical leadership.

What we're looking for

• Formal training or certification in site reliability engineering concepts and 5+ years of applied experience.
• Advanced understanding of site reliability culture, principles, SLI/SLO/SLA, and error budgets for large-scale data systems.
• Advanced knowledge of observability tools such as Grafana, Dynatrace, Prometheus, Datadog, and Splunk.
• Demonstrated experience using enterprise-authorized AI capabilities to improve reliability engineering workflows with strong validation habits.
• Ability to establish team practices for safe AI usage in operations while maintaining security, auditability, and risk controls.
• Advanced knowledge of distributed systems, system design, resiliency, testing, and disaster recovery.
• Strong communication skills to report data-based solutions and mentor others on site reliability principles.
• Experience with cloud platforms (AWS), containerization (Docker, Kubernetes), and CI/CD automation tools like Terraform.