Lead Software Engineer - Java/Python/AI-ML

JPMorganChase is a global leader in financial services, providing strategic advice and products to prominent corporations and governments. As a Lead Software Engineer within the Corporate Global Tax Technology team, you will enhance, build, and deliver trusted market-leading technology products while leading an agile team in optimizing applications and driving innovation.

Responsibilities:

• Design, develop, and implement cloud-based applications and services
• Collaborate with architects and engineers to ensure design solutions are aligned with business goals
• Optimize applications for performance, scalability, and security
• Participate in code reviews and provide constructive feedback to peers
• Stay up to date with emerging Cloud and AI/ML technologies and industry trends to drive innovation and continuous improvement
• Lead communities of practice across Software Engineering to promote awareness and adoption of new and leading-edge technologies
• 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
• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Develops secure high-quality production code, and reviews and debugs code written by others
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems