Senior Lead Software Engineer - AI Development | Engineering Services & Platforms

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 Chief Technology Office (CTO), 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. As the Senior Lead Software Engineer you will work on a rare greenfield opportunity to solve for one of the fastest-growing threats in enterprise technology - Software Dependency Risk. As AI-assisted development accelerates the adoption of open-source and commercial packages across the firm, understanding what software your applications depend on — and whether it's healthy, secure, and compliant — has never been more critical. JPMorgan Chase's answer to that challenge: a firm-wide platform that gives 60,000 developers and their engineering leaders visibility into the dependency health of 6,000+ applications. We are past ideation and into the market — an early product in hand, the roadmap ahead, and a mandate to build something that becomes foundational infrastructure for how the firm manages software risk at scale. Job Responsibilities Take ownership end-to-end — from design through deployment and production support Build and scale core platform capabilities: data ingestion pipelines, dependency analysis services, and developer-facing product features Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors Write production-quality Java on AWS — services, APIs, data pipelines, and the infrastructure that ties them together. Use AI and agentic development tools to develop 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 Collaborate closely with engineers, product managers (US and UK), and designers to ship features that real engineering teams depend on Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years applied experience Hands-on practical experience delivering data-intensive system design, application development, testing, and operational stability Advanced in one or more programming language(s) - Java Experience building core platform capabilities — data ingestion pipelines, dependency analysis services, and developer-facing 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. Ability to tackle design and functionality problems independently with little to no oversight Practical cloud native experience - AWS (Lambda, S3, RDS, or similar services) Preferred qualifications, capabilities, and skills Experience with Greenfield architecture. Experience delivering AI/ML solutions in financial services, capital markets, or operations-focused environments Experience working in highly regulated environments with strong model risk, governance, or control expectations Experience designing scalable system architectures for AI products and platforms across multiple stakeholder groups