Lead Software Engineer, Rates Live Risk Team

As a Software Engineer at JPMorganChase within Global Technology in Tokyo, you will build and operate low-latency, high-availability Python applications used directly by Rates trading desks for intraday risk, PnL, and decision support. You will work closely with traders and key stakeholders in a fast-paced, front-office environment, translating business needs into reliable, performant technology.
The Rates Live Risk & PnL team delivers real-time trading risk and profit & loss capabilities, partnering closely with front-office stakeholders including traders and desk strategists. You will own critical components across the stack—focusing on data ingestion, calculation services, and production operations—ensuring performance, correctness, and resiliency under tight timelines and high business impact. The role requires strong experience supporting trading, and a solid understanding of Rates products and trading workflows.
Job Responsibilities
Support existing trader-facing live risk and PnL applications in production, including day-to-day health, stability, and responsiveness to desk needs
Troubleshoot and resolve issues efficiently across the stack (data, calculations, services, infrastructure dependencies), performing root-cause analysis and implementing durable fixes
Work closely with Rates traders and stakeholders to gather requirements and translate them into high-quality, low-latency technical solutions
Develop and deliver enhancements to live risk and PnL functionality with strong engineering discipline (testing, code quality, performance, secure coding, operational readiness)
Deliver critical Rates risk and PnL projects, coordinating with partner teams to deliver under tight timelines and high business impact
Improve monitoring, alerting, and operational runbooks; participate in incident management and post-incident remediation as needed
Continuously identify and address technical debt, performance bottlenecks, and data quality gaps to improve resiliency and user experience
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.
Required qualifications, capabilities, and skills
Hands-on experience in system design, application development, testing, and operational stability with a track record of leading complex technical initiatives
Strong front-office experience supporting trading (direct engagement with traders, rapid iteration, production ownership)
Strong Rates product and business knowledge (Rates trading workflows, market data, risk, PnL, trade lifecycle)
Advanced proficiency in Python, including building production services and performance-sensitive applications
Strong understanding of real-time/distributed system concepts (e.g., concurrency, messaging/streaming patterns, caching, failure modes)
Experience delivering software in a large corporate environment with strong engineering standards (testing, code quality, security, SDLC)
Practical experience with CI/CD, application resiliency, and secure engineering, including production monitoring and incident response
Proven ability to lead technical design discussions, mentor engineers, and partner effectively with business stakeholders
Strong problem-solving skills and ability to learn quickly and deliver high-quality outcomes under time pressure
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