Lead Software Engineer - Java Fullstack (Risk Framework)

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 within Corporate Technology, you serve as a senior technical lead on an agile team, driving delivery of trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for setting technical direction, owning end-to-end delivery across full stack engineering and DevOps/SRE practices, and proactively reducing operational risk through automation and resilient design. Job responsibilities - Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems - Develops secure and 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. - Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems - Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture - 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 applied experience in Java - Hands-on practical experience delivering system design, application development, testing, and operational stability - Proficiency in one or more programming languages; strong hands-on experience with Java and Spring Boot (or equivalent frameworks) - Strong experience building frontend applications using React and TypeScript, including working with design systems and component libraries - Strong knowledge of DevOps practices: CI/CD, infrastructure automation, release management, and operational readiness - Practical experience applying SRE and reliability engineering practices, including observability, incident response, root cause analysis, and reliability improvements - Solid understanding of the Software Development Life Cycle and agile methodologies, including Application Resiliency and Security fundamentals - Experience designing and operating APIs and integration patterns (REST required; event-driven patterns) - Experience with automated testing across the stack (unit, integration, UI) and quality engineering practices - 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 - Practical cloud-native experience (containers, orchestration, managed services) - Familiarity with distributed systems patterns (resilience, idempotency, rate limiting, caching) - Familiarity implementing security controls in the SDLC (threat modeling, secrets management, secure-by-design) - On-call ownership of service health with a focus on reducing toil through automation - Exposure to data and analytics tooling for operational insights (reliability trends, latency, error analysis) - Exposure modernizing legacy systems without disrupting business delivery