Lead Software Engineer, Rates Live Risk Team

業務内容

• Build and operate low-latency, high-availability Python applications used by Rates trading desks for intraday risk, PnL, and decision support
• Own data ingestion, calculation services, and production operations; ensure performance, correctness, and resiliency under tight timelines
• Support trader-facing live risk and PnL apps in production; monitor health, stability, and desk responsiveness
• Troubleshoot across stack; perform root-cause analysis; implement durable fixes
• Gather requirements from traders and translate into high-quality, low-latency solutions; deliver enhancements with testing, security, and operational readiness
• Improve monitoring, runbooks, and incident management; address tech debt and data quality gaps
• Promote enterprise AI-assisted development practices; establish validation standards and foster code quality and reuse

技術スタック

必須スキル

• Advanced Python development including production services and performance-sensitive, low-latency applications
• Real-time/distributed systems concepts (concurrency, messaging/streaming, caching, failure modes)
• Strong front-office trading domain knowledge (Rates, risk, PnL, trade lifecycle)
• CI/CD, production monitoring, incident response, and secure engineering
• Software development life cycle in a large corporate environment with emphasis on testing and code quality
• Ability to lead technical design discussions, mentor engineers, and collaborate with traders and stakeholders
• Experience with AI-assisted development tools and applying responsible AI practices in delivery

歓迎スキル（該当する場合）

• Data ingestion, data quality, observability, and runbook development
• Incident post-mortems, reliability engineering and on-call readiness
• Practical experience integrating AI-assisted coding/refactoring to accelerate delivery
• Domain familiarity with Rates market data sources and valuation workflows

キャリア成長観点

• Direct exposure to front-office risk systems, enabling deep understanding of Rates trading workflows and business impact
• Opportunity to lead technical design, mentor engineers, and coordinate across partner teams under tight timelines
• Drive adoption of enterprise AI-assisted engineering practices, improving quality, speed, and operational resilience
• Work on high-stakes, low-latency systems, enhancing skills in performance, reliability, and security at scale