JPMorgan Chase
Lead Software Engineer - Full-Stack Java, React & AI
Plano, TX · Full-time · Posted Sep 14, 2026
- Experience
- 5+ years of applied experience; 10+ years of hands-on enterprise software engineering experience.
- Education
- Formal training or certification on software engineering concepts.
About the job
Leads end-to-end design, development, and operation of enterprise managed-account software, while guiding engineers and responsible adoption of AI-assisted engineering practices.
What you'll do
- Own application components across architecture, build, testing, deployment, observability, and production support; deliver secure, resilient, high-performance services and user experiences.
- Lead technical discovery and implementation, partner with cross-functional stakeholders, and coach engineers on technical standards.
- Adopt and guide AI-assisted development; validate generated changes and improve CI/CD, test automation, and developer experience.
What they ask for
- 10+ years of hands-on enterprise software engineering using Java, J2EE, REST APIs, Spring Boot, Hibernate or another ORM, microservices, and relational databases such as Oracle or Sybase.
- 5+ years of applied experience and formal training or certification on software engineering concepts.
- Experience leading approved AI-assisted software development and coaching on safe, compliant use and validation of AI outputs.
- Strong experience with scalable distributed systems, object-oriented design, DDD, microservices, cloud-native architecture, testing, SQL, CI/CD, and Agile delivery.
Nice to have
- Front-end development with React, Angular, TypeScript, JavaScript, HTML, or Redux; event-driven processing with Kafka or comparable platforms.
- AI coding assistants, observability tools, cloud platforms, or financial-services domain knowledge.
Tools
Java, J2EE, REST APIs, Spring Boot, Hibernate, Oracle, Sybase, React
AI focus
Use and lead responsible AI-assisted software development for coding, review, testing, troubleshooting, documentation, and modernization; provide repository and business context to AI agents, protect sensitive data, and validate outputs for correctness, performance, and security.
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