Lead Software Engineer - Full Stack, Java & React

Join a team where your engineering leadership directly impacts how we serve clients and drive innovation  JPMorgan

Chase, you will have the opportunity to grow your career, build meaningful solutions, and collaborate with talented technologists who are passionate about delivering excellence.

As a Lead Software Engineer at JPMorgan

Chase within the Commercial & Investment Bank Technology organization, you will be a core technical contributor and leader on an agile team delivering secure, stable, and scalable technology products that support critical business and client outcomes. You will drive system design and engineering execution across backend and distributed systems and modern UI/console experiences, improving operational excellence through automation, strong software development lifecycle discipline, and responsible use of enterprise-authorized AI-assisted engineering tools.

Job responsibilities

Design and deliver robust full-stack solutions using Java/Spring Boot and React/Type Script, applying strong engineering judgment to meet non-functional requirements including security, resiliency, and performance

Own end-to-end delivery of features across the software development lifecycle including requirements, design, coding, testing, deployment, and production support, with clear accountability for outcomes

Build modern, containerized microservices and platform capabilities, including domain modeling, data design, and secure, high-quality production code while raising standards through code reviews, debugging, and refactoring

Lead technical design and architecture discussions, partnering with stakeholders and aligning designs with relevant architecture standards and forums

Develop backend services and APIs integrating with relational databases, search platforms, and enterprise systems

Create and evolve operational UI/console experiences in React/Type Script, partnering with engineering and product teams to deliver usable workflows

Design and implement event-driven and streaming architectures using Kafka, including topic design, schema/contract strategy, and resilient producer/consumer patterns

Contribute directly to operational stability and reliability by leading incident response and root cause analysis, reducing repeat incidents via preventative controls and automation, and enhancing observability

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

Mentor and develop engineers through design reviews and communities of practice, fostering a culture of diversity, inclusion, opportunity, and respect

Required qualifications, capabilities, and skills

Hands-on expertise in system design, application development, testing, and operational stability in large-scale enterprise environments, including event-driven architectures and streaming patterns (Kafka)
Advanced proficiency in Java and building services with Spring Boot Hands-on experience building modern frontend applications using React with Type Script Experience  building and consuming APIs (REST and/or GraphQL) and integrating with downstream systems

Experience with relational databases and SQL Working knowledge of search/indexing concepts, including designing and querying Elasticsearch indexes

Proficiency across the full software development lifecycle and agile practices, including CI/CD, automation, and modern engineering tools:
Jira, Confluence, IntelliJ IDEA, VS Code, Maven, Git, Jenkins, Spinnaker, Sonar, and enterprise-approved AI code assistants

Hands-on experience with monitoring, tracing, and troubleshooting tools such as log aggregation platforms, API testing tools, and observability dashboards

Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security. Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.

Demonstrated “automation-first” mindset: experience designing and operationalizing enterprise-authorized AI-assisted or agentic automations that reduce manual effort across SDLC, CI/CD, and continuous…