Lead Software Engineer - React, Java

JPMorganChase•Plano, TX

About The Position

As a Lead Software Engineer within JPMorganChase’s Infrastructure Platforms Cloud Foundational Services, you will modernize a web product by delivering secure, stable, and scalable engineering. You will build intuitive, accessible, high-performance React experiences and define scalable front-end architecture informed by product design thinking. You will pair UI capabilities with Java/Spring Boot services, integrating APIs and data sources to meet business objectives, and deliver production-ready conversational and agentic features with attention to safety, observability, and reliability. The role leads the modernization of a low-code product toward micro-frontends, including migration strategy and incremental rollout. It also drives engineering standards and practices across UI architecture, backend services, API integration, testing, CI/CD, cloud collaboration, and operational troubleshooting. You will promote enterprise-authorized AI-assisted engineering practices, establish standards for validating their outputs, and encourage reuse of effective patterns across the team.

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Deep hands-on expertise in React, including component architecture, performance optimization, state management, and accessibility.
Strong backend development experience with Java and Spring Boot, including API design, service patterns, and integration.
Experience redesigning or modernizing web products, including migration from legacy or low-code systems to modern architectures such as micro-frontends.
Strong understanding of API integration across distributed systems, including reliability, latency, observability, and security.
Proven experience with testing and SDLC best practices and implementing or operating CI/CD pipelines.
Practical understanding of cloud infrastructure and operational concerns, including deployment patterns, monitoring/logging, and environment management.
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.
Demonstrated experience leading effective use of approved AI-assisted software development tools, such as for coding, code review, test acceleration, or troubleshooting, and setting expectations for validating outputs for correctness, performance, and security.

Nice To Haves

Experience with design systems, component libraries, and functional and behavioral testing strategies.
Strong product design thinking, including simplifying ambiguous requirements into usable flows and collaborating closely with design and stakeholders.
Experience building conversational or agentic features in production, including UX, tool/function calling, and orchestration patterns.
Familiarity with containers and orchestration concepts such as Kubernetes, as well as modern DevOps practices.
Experience with observability practices such as metrics, logs, and traces, and operational readiness practices such as runbooks and SLOs.

Responsibilities

Lead scalable, maintainable, performant, and accessible React architecture, with attention to WCAG-minded experiences.
Define migration strategy, incremental rollout, and coexistence patterns for modernizing low-code products into micro-frontends.
Deliver consistent user experiences with reusable components and design-system patterns, translating Figma designs into responsive, high-fidelity UI.
Establish and enforce UI engineering standards for state management, code conventions, performance profiling, and telemetry/observability.
Design and build Java/Spring Boot backend services and APIs, including microservices and REST, and event-driven patterns where appropriate, to eliminate repetitive toil.
Integrate APIs and data sources using secure, reliable contracts, including authentication, sessions, caching, pagination, error handling, and rate limits.
Build production-ready conversational and agentic features, including streaming UX, session/history, tool/function calling, and lightweight orchestration with retries and fallbacks.
Drive engineering excellence through end-to-end troubleshooting, durable fixes, SDLC practices, automated testing, CI/CD, and collaboration with cloud/platform teams.
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 engineering, including secure coding, peer review, and automated testing, and promote reuse of effective patterns.
Apply software development lifecycle toolchain capabilities, including authorized AI-assisted development and automation, to improve the value of automation.