Engineering Excellence Sr Lead Software Engineer

At JPMorgan

Chase, we believe the best engineers don't just write code - they raise the bar for how software is built, secured, and sustained across the entire organization. If you're passionate about enabling AI-driven development practices, championing application health, and guiding teams toward a future where vulnerabilities and technical debt are the exception rather than the rule, this is your opportunity to make a lasting impact at one of the world's most influential technology organizations.

As a Senior Lead Software Engineer at JPMorgan

Chase within Corporate Technology, you will serve as a technical authority and role model for building secure, healthy applications at scale - driving the adoption of AI-assisted engineering practices, eliminating vulnerabilities, and leading the modernization of applications through sound architectural principles and proactive technical lifecycle management. Your work will directly shape how engineering teams across the firm design, build, and maintain software in an era of agentic AI and accelerating delivery expectations.

• Drive adoption and governance of enterprise-authorized AI-assisted engineering practices - including agentic coding tools such as Claude, Git Hub Copilot, and similar platforms - across teams to improve code quality, delivery speed, and operational outcomes, while establishing measurable validation standards and promoting reuse of proven patterns within the Software Development Life Cycle toolchain
• Champion application health standards across the engineering organization, serving as a role model for building new applications in a secure, resilient, and well-architected state from inception
• Lead technical lifecycle management efforts, identifying and remediating aging components, deprecated dependencies, and systemic vulnerabilities before they create risk or operational burden
• Partner with architecture and security teams to embed secure-by-design principles into development workflows, ensuring applications meet firm-wide health and compliance expectations at every stage of delivery
• Evaluate and guide teams on the responsible use of agentic AI coding tools, establishing clear expectations for human-in-the-loop validation, output review, and secure handling of sensitive data within AI-assisted workflows
• Contribute to the evolution of engineering standards and toolchain automation, identifying opportunities to reduce manual toil, accelerate release readiness, and improve traceability and auditability across the delivery pipeline
• Mentor and coach engineers at multiple levels, sharing expertise in application health, AI-enabled development, and technical lifecycle practices to elevate team capability and build a culture of engineering excellence
• Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Demonstrated expertise in building and maintaining secure, production-grade applications with a strong command of application health principles, including vulnerability management, dependency hygiene, and technical debt reduction
• Hands-on experience with technical lifecycle management practices, including application modernization, deprecated component remediation, and proactive risk identification across software portfolios
• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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…