Lead Software Engineer

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Lead Software Engineer at JPMorganChase within the Commercial & Investment Bank - Digital & Platform Services, you provide technical leadership and strategic direction for one or more agile teams delivering trusted, market-leading technology products in a secure, stable, and scalable way. You drive architectural decisions, set engineering standards, and mentor engineers while remaining hands-on with critical technology solutions that support the firm's business objectives.

Job responsibilities

• 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
• 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.

• Leads technical design and architecture for complex, cross-functional systems, establishing patterns and standards that teams adopt at scale; Drives software solutions from concept through delivery, resolving ambiguous and novel technical challenges that span multiple domains and business functions

• Architects secure, high-quality production systems and oversees algorithmic design ensuring performance, reliability, and maintainability across distributed environments; Champions and governs the adoption of enterprise-authorized AI coding tools across the team; defines best practices for AI-assisted development (code generation, refactoring, test automation, documentation), establishes validation frameworks through peer review and automated testing, and drives measurable improvements in delivery velocity and code quality

• Shapes the team's SDLC toolchain strategy, identifying and implementing AI-assisted development and automation capabilities that maximize engineering throughput and reduce toil

• Owns architecture and design artifacts for complex, enterprise-scale applications; ensures design constraints, non-functional requirements, and security standards are met across all team deliverables; Synthesizes insights from large, diverse data sets to drive continuous improvement in system architecture, application performance, and engineering processes; presents findings and recommendations to senior leadership

• Identifies systemic technical debt, hidden failure patterns, and architectural risks; develops and prioritizes remediation roadmaps that improve coding hygiene and platform resilience

• Mentors and coaches engineers at all levels, conducting design reviews, driving technical upskilling, and fostering a culture of engineering excellence

• Leads software engineering communities of practice; evaluates emerging technologies and makes build/buy/adopt recommendations to engineering leadership; Partners with product owners, architects, and business stakeholders to translate business strategy into technical roadmaps and execution plans

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 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/outp