Lead Software Engineer - Sr. Java/AWS 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 within the Fraud Risk Group under Trust&Security, 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 conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. Job responsibilities - Design and deliver creative, end-to-end software solutions, applying non-routine problem-solving to break down complex technical challenges into scalable implementations - Architect and build cloud-native, Spring Boot–based microservices with clear domain boundaries, well-defined APIs, and resilient service-to-service integration - Lead the design and implementation of event-driven microservices using Kafka to enable real-time processing, scalable architectures, and reliable cross-functional integration - Develop secure, high-quality production code and raise engineering standards through code reviews, debugging, and hands-on technical leadership - Strengthen production stability by driving root-cause remediation, improving observability, and automating recurring operational and recovery workflows - Partner across product, engineering, and operations to define and deliver microservices that meet performance, availability, and security expectations - Lead structured evaluations with external vendors, startups, and internal teams to assess architectural fit, technical credibility, and integration readiness - Drive communities of practice to accelerate adoption of modern microservices patterns, Spring Boot best practices, and engineering standards across teams - Drive team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes, while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team - Apply 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 - Advanced Java expertise with strong, hands-on Spring Boot experience building and operating microservices in production - Demonstrated experience applying domain-driven design to structure microservices, define service contracts, and manage integration across distributed systems - Proven experience designing and operating event-driven architectures, including Kafka-based streaming and asynchronous microservices communication patterns - Hands-on experience delivering system design, application development, automated testing, and production support for mission-critical services - Proficiency with continuous integration/continuous delivery (CI/CD) practices and automation to improve quality, repeatability, and delivery speed - Strong command of secure software development practices, resiliency engineering, and production-readiness standards for microservices (including reliability and performance considerations) - Practical experience building and operating cloud-native applications (for example, Amazon Web Services (AWS) and/or Cloud Foundry) - 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