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