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