Lead Software Engineer - Python full stack

Design and deliver front-office solutions for real-time and end-of-day risk management and profit and loss (PnL) analysis for the Credit Trading team, spanning both backend services and UI capabilities. As a Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank’s Credit Technology team, you will join an agile engineering group building secure, resilient, low-latency platforms that power front-office risk, trading, and management reporting workflows across Bonds, Loans, Credit Derivatives, and Exotics. You will lead technical delivery across multiple components, including real-time services and the web UI layer used by front-office consumers, while partnering closely with quant, trading, risk, and production management stakeholders. Job responsibilities Build and evolve real-time and end-of-day Risk / PnL capabilities for credit products, including intraday Greeks/sensitivities, VaR inputs, explain/attribution, and scenario/stress runs. Design and deliver low-latency, high-throughput services that publish risk and PnL to front-office consumers with clear SLAs, observability, and operational readiness. Develop distributed microservices and event-driven pipelines that consume market data, trades, and reference data; produce risk measures; and serve APIs to UI and downstream systems. Lead design and delivery of web UIs for real-time risk/PnL workflows using Angular and/or React with TypeScript, including API integration patterns, robust error handling, and resilient user experiences under degraded conditions. Implement UI state management and real-time data patterns (for example, streaming updates, caching, pagination/virtualization) that preserve correctness and performance under high-frequency updates. Own end-to-end technical design with awareness of upstream/downstream dependencies, data contracts, schema evolution, and failure modes across both backend services and UI consumers. 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. Applies strong engineering rigor: test strategy, performance profiling, capacity planning, resiliency patterns, and secure coding across services and UI. Drive UI and service observability: instrumentation, logs/metrics/tracing where applicable, performance monitoring (including client-side performance), and actionable alerting tied to user and system outcomes. Deliver production excellence: incident triage, root cause analysis, runbooks, automated recovery, and measurable reliability improvements, including UI operational readiness and on-call effectiveness. Collaborate with stakeholders to translate business needs into clear technical requirements and deliver iteratively with strong documentation and communication. Mentor engineers through design reviews, code reviews, and coaching; raise the bar on architecture, engineering craftsmanship, and team culture. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 12+ years applied experience Extensive hands-on experience delivering Python services in production, including design, development, testing, troubleshooting, and operational support. Strong knowledge of data structures, algorithms, concurrency, and software design principles; ability to lead design discussions and document architecture. Demonstrated experience across the full SDLC, including CI/CD, automated te