Java Software Engineer III

Serve as an emerging member of an agile team to enhance, build, and deliver technology products with our dynamic, innovative team.

As a Java Software Engineer III at JPMorganChase within the Global Technology team, you will serve as a member of an agile team to design and deliver trusted, market-leading technology products in a secure, stable, and scalable way. In this role, you will help build and evolve the Portfolio Management platform, delivering portfolio management capabilities purpose-built for both Fixed Income and Multi-Asset Solutions. You will support end-to-end portfolio construction and implementation workflows across these asset classes, including intraday portfolio exposure monitoring, order management, scenario-based what-if analysis, order sizing, automated pre-trade compliance checks, and order submission. These capabilities enable portfolio managers and traders to make informed decisions, stay aligned with mandate guidelines, and execute efficiently across Fixed Income and Multi-Asset markets.

You will join the backend engineering team building and maintaining JVM-based microservices that power portfolio analytics, reporting, and trade sizing across these workflows. You will work in a well-structured, multi-module codebase governed by clear conventions, designing and operating resilient services and REST APIs within a modern software development lifecycle that emphasizes quality, security, and operational excellence. Experience with Kotlin is valued; the service is progressively adopting Kotlin, so a willingness to work across both Java and Kotlin is important.

Job responsibilities

• 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)
• 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