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. 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 Formal training or certification on software engineering concepts and 5+ years of applied experience. Hands-on practical experience delivering system design, application development, testing, and operational stability. Advanced in the Java programming language and associated ecosystem. Demonstrated AI fluency: ability to effectively use AI coding assistants, prompt engineering, and LLM-based tools to write, review, and debug production code—and the judgment to validate, refactor, and take ownership of AI-generated output. Experience designing and shipping internal developer tools, CLIs, dashboards, or platforms that improve team velocity or system visibility. Proven track record operating and improving systems with strict uptime requirements (99.9%+ uptime), high request volumes (hundreds of TPS), and complex failure modes—including experience with SLOs, alerting, capacity planning, and graceful degradation. Advanced knowledge of software applications and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, distributed systems, etc.). Ability to tackle design and functionality problems independently with little to no oversight. 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, capabilities, and skills Experience with banking and credit card ecosystems AWS Certification