Lead Software Engineer - Application Owner

As an Application Owner and Lead Software Engineer at JPMorganChase within Public Cloud Engineering, you will own the application’s end-to-end operational integrity including controls, audit readiness, resiliency, recovery, and production outcomes, while remaining hands-on in engineering leadership. You will partner closely with engineering, platform, risk and control, and operations stakeholders to ensure the platform is built and run in a secure, stable, and scalable way. This role suits a hands-on technical leader who enjoys solving complex operational problems, driving remediation to closure, and raising the bar on engineering excellence. Key responsibilities: Serve as the accountable application owner for Atlas Wholesale Platform, ensuring clear ownership of production risk, controls, and operational outcomes. Lead audits and control testing activities for the application, including walkthroughs, evidence preparation, issue responses, and remediation commitments. Own the lifecycle of risk and control findings from intake through remediation and closure, coordinating across internal teams and third parties as needed. Maintain current, high-quality architecture and operational documentation, including high-level design, dependency maps, data flows, control narratives, and runbooks. Own resiliency and disaster recovery planning, including recovery objectives, test execution, after-action reviews, and closure of follow-up actions. Define and continuously improve production readiness standards, including release safety and rollback strategy, dependency awareness, observability requirements, and operational runbooks. Contribute hands-on to design and delivery, including system design, code reviews, automation, and complex troubleshooting, with secure-by-design and reliable-by-default solutions. Build and maintain automation that improves operational outcomes, such as guardrails, health checks, drift detection, remediation automation, and safer deployment patterns. Lead architecture and design evaluations with internal partners and external vendors, assessing technical fit, security posture, and operational viability. 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: Experience building and operating enterprise software in production, including design, development, testing, and operational excellence. Demonstrated experience owning production applications with strong operational accountability, including controls, resiliency and recovery, and remediation tracking. Strong system design fundamentals and cloud-native operational patterns, including scalability, reliability, observability, and dependency management. Hands-on experience with Go-based services and modern CI/CD practices. Experience operating workloads on AWS and Kubernetes or EKS in a production environment. Practical experience with infrastructure as code using Terraform and supporting release safety through automation. Strong understanding of SDLC best practices, including automated testing, change management, and vulnerability management. Ability to lead through influence with no direct reports, align stakeholders, and drive issues to closure. 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 Experience participating in audits and or security compliance assessments, such as PCI or similar. Experience with advanced Kubernetes operational patterns, including policy and guardrails, progressive delivery, service-to-service security, and multi-AZ resilience. Experience using agentic AI developer tools to improve throughput and quality within appropriate governance and secure usage patterns. Experience with additional cloud providers, such as Azure or Google Cloud.