React/Java Lead Fullstack Engineer - Lead Software Engineer

JPMorganChase•Jersey City, NJ

15h

About The Position

As a Lead Software Engineer at JPMorganChase within the Payments Trust & Safety Technology Team, you will serve as a seasoned member of an agile team, designing and delivering trusted, market-leading technology products in a secure, stable, and scalable way. You will be part of the Fraud Protection group, building solutions that help clients protect themselves against fraudulent behavior. The role carries significant business impact and involves collaborative problem-solving across multiple technologies and applications, delivering critical technology solutions across business functions in support of the firm’s objectives. The position focuses on full-stack software engineering, performance engineering, and team adoption of enterprise-authorized AI-assisted engineering practices. You will develop and maintain production software, improve system performance and scalability, and guide teams in validating AI-assisted outputs through secure coding, peer review, and automated testing.

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Hands-on experience delivering system design, application development, testing, and operational stability.
Advanced proficiency in one or more programming languages or technologies, including React, TypeScript, Java, Spring Boot, or microservices.
Hands-on experience with React, TypeScript, JavaScript, Redux, Redux Toolkit, GraphQL, HTML5, CSS3, and REST API integration.
Experience with React Router for frontend routing and Module Federation micro-frontends.
Experience with AWS services such as ECS, EKS, Lambda, S3, EC2, Kafka, and NLB.
Experience with databases.
Demonstrated experience leading effective use of approved AI-assisted software development tools for activities such as coding, code review, test acceleration, or troubleshooting, and setting team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and resiliency and security expectations; experience coaching engineers on safe, compliant adoption.
Proficiency in all aspects of the Software Development Life Cycle.
Advanced understanding of agile methodologies, CI/CD, application resiliency, and security.
In-depth knowledge of the financial services industry and its IT systems.

Nice To Haves

Familiarity with modern front-end technologies.
Strong enterprise architecture design concepts for unified user experiences that leverage data sharing through APIs and Kafka.
Experience with case management workflow and orchestration as a backbone for unified user experiences across multiple cross-functional teams.
Vibe coding experience and conceptual knowledge of developer assistant tools such as GitHub Copilot to improve efficiency.

Responsibilities

Design, develop, test, and troubleshoot creative software solutions, including approaches that go beyond conventional methods to address technical problems.
Develop secure, high-quality production code, and review and debug code written by others.
Monitor and collect performance statistics, and analyze product performance and scalability across different hardware, software, and configurations.
Advise teams on performance and integrate performance considerations into development practices to optimize infrastructure scalability.
Influence leaders and senior stakeholders across business, product, and technology teams.
Anticipate and identify issues that could negatively affect performance, and eliminate or mitigate them.
Plan and develop methodologies for standard performance benchmarks and comparisons.
Design, develop, and implement tools to automate performance measurement and analysis.
Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes.
Establish consistent validation standards for AI-assisted work, including secure coding, peer review, and automated testing, and promote reuse of effective patterns across the team.
Apply knowledge of the Software Development Life Cycle toolchain, including authorized AI-assisted development and automation capabilities, to improve the value realized from automation.