Lead Full-Stack Software Engineer - Java , React & AI

Build technology that matters—at scale, with real business impact. At JPMorganChase, you’ll join a team that values strong engineering, thoughtful collaboration, and continuous learning. You’ll work on modern, cloud-native solutions and help shape patterns that improve reliability, security, and speed of delivery. If you enjoy solving complex problems and raising the bar for how teams build and operate software, you’ll thrive here. You’ll also have opportunities to grow your skills and expand your impact across teams and initiatives. Job Summary: As a Lead Software Engineer within our wider AI, ML and Data Platform (AMDP) teama, you will enhance, build, and deliver trusted, market-leading technology products in a secure, stable, and scalable way. AMDP builds the platforms, services, and engineering foundations that enable teams to develop, deploy, and operate data and machine learning capabilities at scale. You’ll help create reliable, secure, and developer-friendly tooling that accelerates experimentation and production delivery while improving observability and operational rigor. The team works closely with partner engineering groups to standardize patterns and reduce friction across the end-to-end lifecycle—from data pipelines and feature creation to model serving and platform resiliency. Job Responsibilities Deliver creative software solutions through design, development, and technical troubleshooting, breaking down complex problems into well-structured outcomes. Build end-to-end cloud-native, microservices-based applications using current technologies and strong engineering practices. Apply domain modeling techniques to structure software for clarity, testability, and long-term evolution. Develop secure, high-quality production code and review, debug, and improve code written by others. Investigate and resolve issues promptly, ensuring root causes are addressed and problems do not recur. Identify opportunities to eliminate or automate remediation of recurring issues to improve operational stability. Modernize technologies and engineering patterns to keep systems current, maintainable, and scalable. Design resilient architectures that avoid single points of failure and support reliable, scalable performance. Lead communities of practice across Software Engineering to drive awareness and adoption of leading-edge technologies. 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 in software engineering concepts, or equivalent practical experience in software engineering, including system design, application development, testing, and operational support Proficiency in back-end technologies (e.g., Java, Spring Boot, Node.js) and experience building microservices-based applications Proficiency in front-end technologies (e.g., HTML, CSS, JavaScript, TypeScript, React, Angular) Experience working with cloud platforms (e.g., AWS, Azure, GCP), distributed systems, and web technologies Experience building and integrating RESTful APIs and web services, including working with JSON and WebSockets as needed Hands-on experience designing and building scalable applications using SQL and NoSQL databases Experience using agile development methodologies (e.g., Scrum) and applying software development life cycle practices Proficiency in automation and continuous delivery methods, including CI/CD practices Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices Preferred Qualifications, Capabilities, and Skills Strong soft skills, including presentation, negotiation, mentoring, and stakeholder management Strong problem-solving, analytical, and communication skills Ability to drive broader impact by sharing and contributing best practices Experience in the banking domain AWS certification