Sr Lead Software Engineer:Java/AI

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer at JPMorganChase within the Consumer and Community Banking Cobranded Cards Team, you will play a crucial role as part of an agile team dedicated to enhancing, building, and delivering trusted, market-leading technology products in a secure, stable, and scalable manner.

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

• Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors.
• Develops secure and high-quality production code, and reviews and debugs code written by others—leveraging AI-assisted development tools (e.g., LLM-based code generation, automated debugging, test synthesis) to accelerate delivery without sacrificing quality.
• Drives decisions that influence product design, application functionality, and technical operations and processes.
• Designs, builds, and maintains internal tooling and developer platforms that measurably improve engineering productivity, observability, and operational efficiency across teams.
• Serves as a function-wide subject matter expert in one or more areas of focus, including the practical and responsible application of AI/ML techniques in software engineering workflows.
• Owns and operates systems that run 24x7 at high traffic scale—including on-call responsibilities, incident response, and post-mortem analysis—with a strong bias toward proactive reliability improvements over reactive firefighting.
• Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle.
• Influences peers and project decision-makers to consider the use and application of leading-edge technologies, including emerging AI tooling and automation strategies.
• 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.