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Lead Site Reliability Engineer - Operations Excellence for AI Platforms
lead · Operations
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 AIML 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.
- Consistently models and champions site reliability culture and practices, documents and shares knowledge within your organization via internal forums and communities of practice
- Leads initiatives to improve the reliability and stability of your team’s applications and platforms using data-driven analytics to improve service levels, proactively identifying and solving technology-related bottlenecks in areas of expertise
- Drives collaboration with your team to identify comprehensive service level indicators and the stakeholder partners to establish reasonable service level objectives and error budgets with your customers
- 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.
- Serves as the main point of contact during major incidents for your application and has the skills to identify and solve the issue quickly to avoid financial loss to the business
- Offers a high level of technical expertise within one or more technical domains and provides advice and mentorship to other engineers
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
- Formal training or certification on site reliability engineering concepts and advanced applied experience
- Demonstrated proficiency in reliability, scalability, performance, security, enterprise system architecture, toil reduction, and other site reliability best practices
- Fluent in at least one programming language such as: Python, Java/Spring Boot, .Net
- Demonstrated 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.
- Proficient knowledge and experience in observability such as white and black box monitoring, service level objective alerting, and telemetry collection
- Proficient with continuous integration and continuous delivery practices and tooling
- Proficient with container and container orchestration
- Experience with troubleshooting common networking technologies and issues
Advanced knowledge of software applications and technical processes with emerging depth in one or more technical disciplines, and actively self-educates to evaluate and recommend suitable new technologies
Preferred qualifications, capabilities, and skills
- Experience leading reliability programs across multiple services/teams (platform-level or domain-level).
- Experience building automation for operations (runbook automation, event correlation, alert tuning, self-healing patterns).
- Exposure to agentic AI / AIOps concepts (workflow orchestration, decision support, safe automation patterns).