Lead Software Engineer - Java Fullstack (Risk Framework)
Summary
Lead Java fullstack engineer driving secure, scalable risk framework solutions at JPMorgan Chase. Owns technical direction, DevOps/SRE practices, and AI-assisted engineering workflows to enhance code quality, reliability, and operational stability.
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
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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.
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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.
- 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.
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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
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Practical cloud-native experience (containers, orchestration, managed services)
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Familiarity with distributed systems patterns (resilience, idempotency, rate limiting, caching)
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Familiarity implementing security controls in the SDLC (threat modeling, secrets management, secure-by-design)
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On-call ownership of service health with a focus on reducing toil through automation
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Exposure to data and analytics tooling for operational insights (reliability trends, latency, error analysis)
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Exposure modernizing legacy systems without disrupting business delivery