Vista previa de la oferta
Lead Software Engineer - Java Fullstack (Risk Framework)
lead · Technology / Software Development
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.
- 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
- 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