Lead Software Engineer - Agentic AI

JPMorganChase is a financial services firm serving consumers, businesses, and other organizations through a broad range of financial products and technology solutions. The Lead Software Engineer will lead the development of agentic AI platform capabilities within the Consumer and Community Banking Deposits 2.0 platform, including orchestration, APIs, SDKs, integrations, and production deployment. The role also establishes quality, security, governance, reliability, and responsible AI practices while guiding engineering teams and stakeholders.

Responsibilities:

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
• Define and drive the platform roadmap for agent-based capabilities, focusing on measurable outcomes, reliability, and usability
• Lead end-to-end delivery of core agent platform components, including software development kits, reference implementations, and integration patterns
• Partner with product, engineering, risk, and control stakeholders to align requirements, prioritize trade-offs, and unblock execution
• Establish quality, performance, and operational standards for agent workloads, including monitoring, testing, and incident readiness
• Translate experimentation into production by driving clear architecture decisions, scalable designs, and repeatable deployment practices
• Guide responsible development practices by embedding governance, privacy, and model risk considerations into platform design
• Communicate technical strategy and progress to senior stakeholders with clarity, data, and pragmatic recommendations
• 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