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/ML Data Platforms team, you will be instrumental in building scalable, resilient and market-leading data solutions. You will engage in root cause analysis, production changes, budgetary considerations, and staffing challenges. Your experience will be vital in managing and mentoring team members to drive strategic change, both within your team and in partnership with colleagues across JPMorgan Chase & Co.s global network of innovators.
Job Responsibilities:
- Expertise in application development and support with multiple technologies such as Databricks, Snowflake, AWS, Kubernetes, etc.
- Coordinate incident management coverage to ensure effective resolution of application issues.
- Collaborate with cross-functional teams to perform root cause analysis and implement production changes.
- Mentor and guide team members to foster innovation and strategic change.
- Develop and support AI/ML solutions for troubleshooting and incident resolution.
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Required qualifications, capabilities and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Proficient in site reliability culture, principles and expertise in running production incident calls and managing incident resolution.
- Experience in observability such as white and black box monitoring, service level objective alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, and others
- Strong understanding of SLI/SLO/SLA and Error Budgets
- Proficiency in Python or PySpark for AI/ML modeling.
- Must be able to reduce toil by building new tools to automate repeated tasks.
- Hands-on experience in system design, resiliency, testing, operational stability, and disaster recovery
- Understanding of network topologies, load balancing, and content delivery networks.
- Awareness of risk controls and compliance with departmental and company-wide standards.
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations experience coaching engineers on safe, compliant adoption within delivery practices
Preferred qualifications, capabilities and skills
- Hands on experience an SRE or production support role with AWS Cloud, Databricks, Snowflake or similar Technologies.
- AWS, Snowflake or Databricks certifications.
- Familiar on how to implement site reliability within an application or platform
