Lead Software Engineer - Quant Developer

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase's Asset and Wealth Management team , you are an integral part of an agile team that works to enhance, build, and deliver trusted, market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for delivering critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. Job Responsibilities Execute creative software solutions, design, development, and technical troubleshooting—thinking beyond routine or conventional approaches to build solutions or break down technical problems. Deliver end-to-end solutions in the form of cloud-native, microservices-based applications, leveraging the latest technologies and best industry practices. Use domain modeling techniques to build best-in-class business products, structuring software for clarity, testability, and evolution. Develop secure, high-quality production code; review and debug code written by others. Identify opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems. Lead communities of practice across Software Engineering to drive awareness and adoption of new and leading-edge technologies (including AI-enabled software delivery approaches). Add to team culture of diversity, opportunity, inclusion, and respect. Promptly investigate and resolve issues, ensuring they do not resurface. Continuously update technologies and patterns to keep systems current. Design and build scalable, secure, and reliable solutions by leveraging modern architectural patterns that enable zero-downtime releases and optimize data performance. Engineer and operationalize AI-enabled capabilities (e.g., agentic workflows, tool use, orchestration, evaluation/guardrails) to improve developer productivity and/or product functionality. 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. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years of hands-on experience in software engineering, including system design, application development, testing, and operational support. Strong proficiency in back-end development with Python and Java, with experience building microservices-based applications and APIs (e.g., RESTful services, JSON). Strong experience building on AWS (or comparable cloud), including designing, deploying, and operating distributed, cloud-native systems. Experience with distributed systems and web technologies (e.g., RESTful APIs and web services, WebSockets). Hands-on experience designing and building scalable applications using SQL and NoSQL databases. Experience with agile development methodologies (e.g., Scrum) and an understanding of the software development life cycle. Understanding of application resiliency and designing for reliability/availability. Proficient in all aspects of the Software Development Life Cycle, including secure development practices. Advanced understanding of agile engineering practices such as CI/CD, Application Resiliency, and Security. 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 Preferred qualifications, capabilities, and skills Familiarity with modern front-end technologies