Java Software Engineer II

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 II 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 Fixed Income Solutions. You will support end-to-end portfolio construction and implementation workflows 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 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 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 Solutions