Sr Lead Software Engineer - AI-Native Component Engineering

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 Chief Technology Office, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

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. 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. Drives decisions that influence the product design, application functionality, and technical operations and processes. Serves as a function-wide subject matter expert in one or more areas of focus. Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years applied experience. Hands-on practical experience delivering system design, application development, testing, and operational stability. 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 expectations; experience coaching senior engineers/leads on compliant usage patterns and controls. Advanced skills in Java, JavaScript, or Python. Proficiency in automation and continuous delivery methods. Proficient in all aspects of the Software Development Life Cycle. Advanced understanding of agile methodologies such as CI/CD, application resiliency, and security. Demonstrated proficiency in a technical discipline such as cloud, artificial intelligence, or machine learning. Practical cloud-native experience. Ability to tackle design and functionality problems independently with little to no oversight. Preferred qualifications, capabilities, and skills Experience building SDKs, frameworks, or developer platforms that other engineering teams consume. Experience with AI-native tooling and AI-assisted development workflows. Demonstrated code shipped to production that leverages AI or builds AI capabilities.