Java Support Engineer Jobs Near Me

Build the next generation of liquidity trading on Morgan Money—joining a hands‑on Java engineering team at JPMorganChase in Asset & Wealth Management where you’ll partner with trading stakeholders, ship frequently, and use enterprise-authorized AI‑assisted practices to improve reliability, speed, and client outcomes.

As a Lead Software Engineer (Java) at JPMorganChase within Morgan Money in Asset & Wealth Management, you will advance next‑generation liquidity trading capabilities for corporate clients across regions. You will be a hands‑on engineer building resilient, secure, high‑performing Java services while partnering closely with investment and trading stakeholders to deliver measurable business outcomes. You will help strengthen engineering practices and reliability in a fast‑paced environment with frequent production releases.

• Partner with investment and trading teams to translate business objectives into durable, measurable technical outcomes
• Design, build, and operate low‑latency backend services in Java with a focus on scalability, resiliency, and secure‑by‑design engineering
• Own end‑to‑end delivery of features from design through production release, ensuring operational readiness and strong customer outcomes
• Drive architecture and engineering decisions that improve performance, reliability, and long‑term maintainability across services
• Establish and uphold disciplined engineering practices, including automated testing, code review, and continuous integration and delivery
• Improve observability and incident readiness through effective logging, metrics, tracing, and production diagnostics to reduce time to detect and recover
• Drives team adoption of enterprise-authorized AI‑assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI‑assisted code review/refactoring, test strategy acceleration, incident/root‑cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
• 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

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Hands‑on experience delivering production‑grade backend services using Java, with strong object‑oriented design and debugging skills
• Experience building scalable distributed systems with a focus on low‑latency performance, resiliency patterns, and failure‑mode thinking
• Proficiency with modern Java frameworks (e.g., Spring) and dependency injection patterns to build maintainable services
• Demonstrated experience implementing strong testing practices (e.g., unit, integration, and contract testing) and using automated quality gates
• Experience with messaging and streaming technologies (e.g., Kafka or enterprise messaging platforms) and event‑driven design patterns
• Strong understanding of secure coding practices and the ability to design and implement controls that reduce operational and technology risk
• Demonstrated experience leading effective use of approved AI‑assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

• Experience building and operating services on cloud platforms, including reliability and cost‑aware engineering practices
• Proficiency in performance engineering (profiling, latency analysis, concurrency tuning) for high‑throughput Java services
• Experience with observability platforms and practices (metrics, tracing, alerting strategy) to improve service health and incident response
• Familiarity with liquidity, trading, or capital markets concepts and the ability to partner effectively with front‑office stakeholders
• Proficiency in Python for automation, tooling, or data analysis to support engineering productivity and reliability outcomes