Develop secure, scalable Java applications and services supporting credit risk workflows. Build React front-end features, optimize SQL queries, design resilient event-driven systems with Spring Boot and Kafka, and apply enterprise-authorized AI coding tools responsibly. Collaborate with product, architecture, and business teams; produce architecture artifacts; troubleshoot complex systems; participate in code reviews; and improve software quality, automation, security, and operational reliability.
Join a team where your engineering skills directly influence how one of the world's most respected financial institutions manages risk at scale. At JPMorganChase, you will work alongside talented engineers and business partners to build technology that matters — solutions that are secure, resilient, and designed to perform under the most demanding conditions. Here, your growth is supported, your ideas are valued, and your work has real impact across the firm and the clients we serve.
As a Software Engineer III at JPMorganChase within the Corporate Technology Credit Risk team, you will serve as a seasoned member of an agile engineering team, designing and delivering trusted, market-leading technology products in a secure, stable, and scalable way. You will contribute to critical technology solutions that support the firm's credit risk objectives, working across multiple technical areas to drive meaningful outcomes for the business. This is an opportunity to deepen your technical expertise, collaborate with cross-functional partners, and help shape the tools and platforms that underpin sound financial decision-making at a global scale.
Job responsibilities
- Design and develop secure, high-quality Java-based applications and services that support credit risk workflows, ensuring code is maintainable, performant, and aligned to architectural standards
- Build and maintain responsive front-end features using React, delivering intuitive user experiences for risk analysts and business stakeholders
- Write and optimize SQL queries and data models to support reporting, analytics, and data-driven decision-making across credit risk platforms
- Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards
- Apply 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
- Produce architecture and design artifacts for complex applications, ensuring design constraints are met throughout the software development process
- Proactively identify hidden problems and patterns in data, using insights to drive improvements to coding hygiene, system architecture, and application reliability
- Collaborate with product, architecture, and business teams to gather requirements, synthesize findings, and deliver solutions that address real credit risk challenges
- Contribute to a culture of continuous improvement by participating in code reviews, sharing reusable patterns, and supporting team members in adopting best practices
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and proficient applied experience
- Hands-on practical experience in system design, application development, testing, and operational stability across enterprise-scale environments
- Proficiency in Java for back-end application development, including experience with modern frameworks and object-oriented design principles
- Hands-on experience with Spring Framework/Spring Boot, Kafka, event-driven architecture, and building resilient systems
- Experience developing and maintaining front-end solutions using React or comparable modern JavaScript frameworks
- 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
- Experience writing and optimizing SQL queries in support of application development, data analysis, or reporting
- Solid understanding of agile methodologies and software delivery practices, including continuous integration and continuous delivery, application resiliency, and secure coding standards
- Demonstrated ability to debug, troubleshoot, and maintain code in a large, complex corporate technology environment
Preferred qualifications, capabilities, and skills
- Familiarity with credit risk concepts, financial risk frameworks, or related domain knowledge within financial services
- Experience working with cloud platforms such as AWS or Azure in an enterprise engineering environment
- Exposure to data visualization tools or reporting frameworks used to communicate risk insights to business stakeholders
- Knowledge of containerization and orchestration technologies such as Docker or Kubernetes
- Experience contributing to or leading code reviews with a focus on quality, security, and knowledge sharing
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