Lead UI Engineer — React, TypeScript, Design-Led, AI-Driven Interfaces

JPMorganChase•Jersey City, NJ

About The Position

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 Corporate – Chief Analytics Office Creative Tech team, 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. The Creative Tech team is dedicated to strategically integrating generative AI across JPMorganChase, driving innovation in major internal applications that leverage artificial intelligence and machine learning to boost employee productivity. Our work sits at the intersection of cutting-edge AI capabilities and the real, day-to-day needs of a global enterprise.

Requirements

Formal training or certification on software engineering concepts and 7+ years applied experience
Demonstrate deep expertise in JavaScript/TypeScript and advanced proficiency in React, with a track record of building and maintaining complex applications.
Possess a strong background in modern web development, including responsive design, performance optimization, REST APIs, and asynchronous data flows.
Write clean, testable code with meaningful unit and integration test coverage.
Apply Web Content Accessibility Guidelines (WCAG) accessibility standards and best practices to ensure inclusive user experiences.
Work effectively in agile, distributed teams, contributing to sprint planning, code reviews, and iterative delivery.
Communicate clearly with stakeholders at all levels, translating technical complexity into accessible language.
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

Nice To Haves

Hands-on experience with design systems, component libraries, and delivering consistent UI/UX at scale.
Experience with state management libraries such as Redux, Zustand, or React Query.
Familiarity with AI/ML APIs or integrating LLM-based features into production applications.
Comfort working with design tools such as Figma for design collaboration and handoff.
Design, implement, and manage CI/CD pipelines to automate software build, test, and deployment processes.

Responsibilities

Lead the development of sophisticated AI-enhanced user interfaces for large-scale internal tools.
Translate designer vision into polished, pixel-perfect front-end experiences, demonstrating a keen eye for detail, aesthetics, and high-quality UI execution.
Collaborate cross-functionally with product, design, and backend engineering teams to deliver seamless, forward-thinking solutions.
Own the codebase by writing clean, efficient, and reusable React code, and uphold high standards through rigorous code reviews.
Drive quality through thorough testing, proactive troubleshooting, and continuous optimization of performance and reliability.
Mentor and inspire junior engineers, share best practices, and foster continuous learning.
Communicates effectively by gathering requirements, providing updates, and translating complex technical concepts for stakeholders.
Stays ahead of the latest trends in front-end and AI technologies, and champion continuous improvement.
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.