Lead Software Engineer - Java / Python - Equity Derivatives - Front Office Quant Developer

J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world. 

Join us and elevate your software engineering career. You’ll design market-leading technology products that drive innovation in financial services. At JPMorganChase, you’ll collaborate with talented teams, grow your skills, and make a real impact. We value your expertise and offer a dynamic environment where your ideas drive progress. Be part of a team that supports your growth and celebrates your success.

As a Lead Software Engineer in the Markets Technology team, you will design and deliver secure, stable, and scalable technology solutions that support our business objectives. You will work within an agile team, collaborating across functions to create trusted products. You will help shape the architecture and development of complex applications, ensuring high standards and continuous improvement. Your contributions will directly impact our clients and the success of our business.

Job Responsibilities:

• Own production outcomes for critical Equity Derivatives workflows, including monitoring, incident triage, stakeholder communication, mitigation, and root cause follow-through
• Execute software solutions through design, development, testing, release, and technical troubleshooting
• Create secure, high-quality production code and maintain services and integrations with critical systems
• Produce architecture and design artifacts for complex applications, ensuring alignment with design constraints and operational requirements
• Collaborate with front office, operations, and technology teams to gather requirements, define acceptance criteria, and align on product specifications
• Drive user acceptance testing (UAT) planning and execution for changes impacting pricing, booking, and risk, including test strategy, defect triage, evidence collection, and sign-offs
• Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, while establishing consistent validation standards and promoting reuse of effective patterns
• Apply knowledge of the Software Development Life Cycle toolchain, including AI-assisted development and automation capabilities, to maximize automation value
• Maintain strong release discipline, including regression assessment, rollback/fallback planning, post-deployment verification, and observability improvements
• Gather and synthesize data and telemetry to develop reporting and metrics that improve stability, quality, and delivery predictability
• Identify hidden failure patterns in production and drive improvements in coding hygiene, system architecture, and operational readiness

Required Qualifications, Capabilities, and Skills:

• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
• Strong software engineering fundamentals with hands-on experience across the software development lifecycle, including system design, development, testing, release, and production support
• Proficiency in Python, Java, and JavaScript or TypeScript
• Experience building and supporting production-grade systems
• Experience in large-scale enterprise environments, developing, debugging, and maintaining code and data integrations using modern programming and database querying languages
• Familiarity with engineering frameworks and tooling such as JUnit, Maven, Spring Boot, Spring Data JPA, Spring Batch, and Hibernate
• Demonstrated experience leading effective use of approved AI-assisted software development tools, 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, secure handling of inputs and outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
• Strong analytical thinking, structured problem solving, and debugging skills
• Excellent communication and stakeholder management skills
• Experience delivering in an Agile environment, managing multiple priorities and projects, and producing clear documentation

Preferred Qualifications, Capabilities, and Skills:

• Formal training or certification in software engineering concepts
• React experience