Lead Software Engineer - UI (React & Python)

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As a Lead Software Engineer at JPMorganChase within the Commercial & Investment Bank, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Build and evolve real-time Risk / PnL UI workflows 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 consuming 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, robust error handling, and resilience under degraded conditions.
• Implement UI state management and real-time data patterns (streaming updates, caching, pagination/virtualization) for correctness and performance under high-frequency updates.
• Own end-to-end technical design across UI and services, including data contracts, schema evolution, dependencies, and failure modes.
• Drive adoption of enterprise-authorized AI-assisted engineering practices with strong validation standards (secure coding, peer review, automated testing) and pattern reuse. Drive observability and production excellence: instrumentation, monitoring/alerting, incident triage, root-cause analysis, runbooks, and reliability improvements.
• Partner with stakeholders to translate business needs into clear requirements and deliver iteratively with strong documentation and communication.
• 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

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Strong front-end engineering experience with Angular and/or React and TypeScript, including component design, UI testing, and maintainability.
• Strong SDLC experience in an enterprise environment: CI/CD, automated testing, release management, and production support with governance/controls.
• Experience building real-time systems (messaging/streaming, caching, low-latency APIs) and integrating UIs with backend APIs using contract-driven development and safe rollout patterns.
• Proficiency in performance tuning across the stack (services: CPU/memory/IO; UI: responsiveness/render performance) and designing for throughput/backpressure/graceful degradation.
• Experience leading effective use of approved AI-assisted development tools, with standards for validating AI outputs (correctness, performance, security) and responsible AI practices.
• IAM/SSO integration experience (OAuth2/OIDC and/or SAML), JWT/session management, and RBAC/entitlements; MFA-aware flows and secure session lifecycle controls
• 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 …