Software Engineer III— Java FullStack

As a Software Engineer III at JPMorgan Chase within the CONSUMER & COMMUNITY BANKING, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way.

Job responsibilities

• Deliver end-to-end features across React (TypeScript) UI and Java/Spring Boot services, from design through production support readiness.
• Lead technical design for moderately complex initiatives (API/service boundaries, data models, integration patterns) and document key decisions.
• Build and maintain automated testing (unit/integration/contract and UI tests as applicable) and drive defect prevention.
• Build and improve CI/CD pipelines (build/test/scan/deploy, quality gates, environment promotion/rollback) to improve release reliability.
• Use JP Morgan Chase-approved AI-assisted development tools (e.g., Claude AI where approved) to accelerate refactoring, change delivery, test generation, and documentation with rigorous human validation.
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
• 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.
• Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
• Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
• Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture

Required qualifications, capabilities, and skills  

• Formal training or certification on software engineering concepts and 3+ years applied experience 
• Strong hands-on experience with Java and Spring Boot, REST API design, and distributed systems fundamentals.
• Hands-on experience with React + TypeScript and integrating UI with backend APIs.
• Working experience with AWS and cloud-native fundamentals (security/IAM concepts, monitoring/operations basics).
• Proven experience implementing CI/CD with automated tests and quality gates (e.g., Jenkins/GitHub Actions/GitLab CI).
• Strong troubleshooting skills across UI/service layers and disciplined use of AI coding assistants (Claude AI or equivalent where approved) with verification.
• 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.
• Overall knowledge of the Software Development Life Cycle
• Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

Preferred qualifications, capabilities, and skills 

• Experience with containers and orchestration (Docker, ECS/EKS/Kubernetes) and deployment automation.
• Experience with event-driven systems (Kafka and/or AWS messaging such as SQS/SNS) and asynchronous processing patterns.
• Experience with security best practices (OWASP awareness, dependency governance, secrets management, authN/authZ patterns).
• Experience defining/operating reliability practices (SLOs/alerting, incident learning, resiliency patterns like retries/circuit breakers).
• Track record improving developer productivity (reusable libraries, pipeline templates, mentoring/technical leadership within a squad)