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.