JPMorgan Chase—Lead Software Engineer -Java, AWS

Job description

Description: 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 Payments Trust & Safety Technology Team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You will be part of the Fraud Protection group, building solutions for clients to protect themselves against fraudulent behavior. Drive significant business impact through your capabilities and contributions, and apply collaborative problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. 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 Required qualifications, capabilities, and skills - Formal training or certification on Software Engineering concepts and 8+ years applied experience - 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 - Hands-on practical experience delivering system design, application development, testing, and operational stability - Advanced hands on development experience in Java, Spring boot, Microservices - Experience with AWS Services such as ECS/EKS, Lambda, S3, EC2, Kafka, NLB - Experience with Databases - Proficient in all aspects of the Software Development Life Cycle and advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security - Experience working with AI agents/copilots for engineering - In-depth knowledge of the financial services industry and their IT systems Preferred qualifications, capabilities, and skills - Strong enterprise architecture design concepts to allow for unified user experience leveraging data sharing capabilities with API, Kafka - Case management workflow and orchestration to serve as the backbone of a unified user experience across multiple cross-functional teams - Vibe coding experience and conceptual knowledge while leveraging developer assistant tools like GitHub Copilot to allow for efficiency gains