Lead Software Engineer

hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role.

JOB DESCRIPTION

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 JPMorgan

Chase 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/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
• Proven track record of leading AI-assisted software development adoption within engineering teams, including establishing governance frameworks, validation standards, and reusable patterns; demonstrated ability to critically evaluate AI-generated outputs for correctness, performance, security, and architectural fit;
  Deep understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of…