Participate in designing and developing scalable and resilient JVM microservices (Java and/or Kotlin) using the Spring Boot ecosystem to contribute to continual, iterative improvements for product teams
Produce or contribute to architecture and design artifacts for applications while ensuring design constraints are met by software code development
Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
Identify hidden problems and patterns in data and use these insights to drive improvements to coding hygiene and system architecture
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
Contribute to software engineering communities of practice and events that explore new and emerging technologies
Add to team culture of diversity, opportunity, inclusion, and respect
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 in a production environment
Proficiency in coding in Java (Kotlin experience or willingness to learn Kotlin is strongly valued)
Experience developing, debugging, and maintaining code in a large corporate environment with modern programming languages and database querying languages (SQL)
Strong fundamentals with relational databases, including schema design, writing and tuning SQL, and managing schema changes via migrations (e.g., Liquibase)
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Overall knowledge of the Software Development Life Cycle, including code review, testing strategies, release practices, and operational support
Understanding of agile methodologies and modern engineering practices such as CI/CD, application resiliency, and security
Working knowledge of cloud concepts and services (e.g., AWS fundamentals such as RDS, S3, CloudWatch, and container platforms such as Kubernetes)
Familiarity with production readiness practices including observability (metrics, tracing, logging) and incident/problem management in distributed systems
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations
Preferred qualifications, capabilities, and skills
Familiarity with modern front-end technologies
Exposure to event-driven architectures and messaging (e.g., Kafka)
Experience with containerization and platform engineering patterns (Docker, Kubernetes)
Domain experience in financial services, including familiarity with capital markets, trading workflows, and portfolio management concepts—especially in Fixed Income and Multi-Asset Solutions
About J.P. Morgan
Parent group profile
JPMorgan Chase
Banking323028 employees
Global financial services and investment banking firm.