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