Lead Software Engineer - AWS/SRE Engineer

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorgan Chase within the Deposits team of Consumer & Community Banking Division, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. Job responsibilities Lead Live Site Management and cloud production management practices, including production readiness, change governance, incident response, problem management, and operational risk reduction. Define and improve SRE practices, including service-level indicators, service-level objectives, error budgets, capacity planning, and reliability engineering. Advance observability across applications and infrastructure through effective logging, metrics, distributed tracing, dashboards, alerting, and automated anomaly detection. Drive major incident response, root-cause analysis, corrective actions, and the systematic elimination of recurring production issues. Establish production health, availability, performance, and resiliency measures that provide actionable insight to engineering and business leadership. Promote team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes, while establishing consistent validation standards and promoting reuse of effective patterns across the team. Apply knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized through automation. Shape the end-to-end AWS cloud architecture strategy across all product components ; Strengthen cloud cost strategy and optimization across the product. Advance the resiliency strategy, including multi-region, active-active, failover/failback, and disaster recovery. Evolve the automation and infrastructure-as-code strategy ; Mature AI integration across engineering and product workflows. Raise the bar on shared architectural standards and decision-making across teams ; Sharpen how complex technical strategy is distilled into leadership-facing narratives. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years applied experience Deep, broad AWS expertise across compute, data, networking, security, and observability, with proven production architecture experience. Systems thinking, including the ability to reason about trade-offs, failure modes, and dependencies across an entire product. Strategic command of cost, scalability, automation, reliability, and AI integration as connected levers. Hands-on depth to validate and prototype ideas, not just advise. Executive communication and rigorous, independent judgment in a regulated, high-stakes environment. Proven experience managing cloud-hosted production services and leading Live Site Management practices in complex, business-critical environments. Strong SRE and observability expertise, including SLI/SLO definition, error budgets, telemetry, distributed tracing, alert design, capacity management, and performance engineering. Demonstrated ability to lead high-severity incident response, perform root-cause analysis, and drive durable remediation across multiple engineering teams. Experience establishing production readiness standards, operational controls, runbooks, dashboards, and measurable service health objectives. Demonstrated experience leading effective use of approved AI-assisted software development tools, 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, secure handling of inputs and outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.