Lead Site Reliability Engineer - Operations Excellence for AI Platforms

Shape the future of a leading firm, promoting impact in a high performance environment focused on site reliability operations excellence.

Assume a critical role in defining the future of a globally recognized firm and have a direct and significant effect in a realm tailored for top achievers in site reliability. As a Lead Site Reliability Engineer at JPMorgan Chase within the AI Machine Learning and Data platform team, you hold a leadership role in your team, demonstrate strong knowledge across multiple technical domains, and advise others on the technical and business issues facing them. Take lead and conduct resiliency design reviews, break up complex problems into digestible work for other engineers, act as a technical lead for medium to large-sized products, and provide advice and mentoring to other engineers. Job Responsibilities Design and implement solutions to enhance the reliability and scalability of AI/ML platforms and applications to accommodate fast growing demands. Partner with product engineering teams to ensure the AI/ML systems are reliable and high performing. Develop observability, security, automation and fin-ops tools and orchestration. Provide strategic technology leadership by defining and evaluating standards and architecture for reliability, observability and automation frameworks. Build strong cross-functional relationships that foster engagements across the organization and deliver solutions to user problems. Debug and solve issues in a production environment, identify root cause and remediate. Participates in on-call rotations, incident management and escalation workflows. Take full ownership of problems, develop solutions, and acquire new knowledge to complete the task. Mentor and guide junior engineers Uses enterprise-authorized AI capabilities within the work environment to accelerate major-incident triage, troubleshooting, and post-incident analysis, validating outputs and handling operational data according to sensitivity and security requirements. Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., CI/CD quality checks, test/validation automation, and operational readiness), ensuring traceability/auditability, resiliency, and security controls. Required qualifications, capabilities, and skills Formal training or certification on site reliability engineering concepts and 5+ years applied experience. Expertise in SRE principles, reliability, scalability and performance of application and infrastructure. Expertise in programming with Python and Infrastructure as Code, tools such as Terraform. Demonstrable experience using enterprise-authorized AI capabilities within the work environment to improve SRE workflows (e.g., incident investigation support and knowledge capture) with strong validation habits and awareness of data sensitivity. Ability to evaluate AI-assisted operational recommendations for correctness and risk, define appropriate guardrails for team usage, and ensure outcomes align to resiliency and security expectations. Experience in architecting distributed systems and cloud-native architecture in AWS. Systematic problem-solving and troubleshooting skills in a complex system. Excellent communication skills and ability to represent and present business and technical concepts to stakeholders. Preferred qualifications, capabilities, and skills • Prior experience working in AI, ML, or Data engineering. Expertise in container orchestration/Kubernetes. • Prior experience developing Automation frameworks/AI Ops • Prior experience building observability and telemetry tools. • Previous experience as an SRE in a dynamic technology company or startup