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 testing, release management, and production support in a controlled enterprise environment.
• Proven ability to build secure, stable, maintainable systems in a large enterprise environment with strong controls, auditability, and SDLC governance.
• Experience building real-time systems, including messaging/streaming concepts, caching, and low-latency APIs.
• Hands-on front-end engineering experience building and operating modern web applications using Angular and/or React and TypeScript, with strong proficiency in component design, UI testing, and maintainability.

• Experience integrating UIs with backend APIs, including authentication-aware API access, contract-driven development, backward compatibility, and safe rollout patterns. 

  Proficiency with profiling and performance tuning across the stack (CPU/memory/IO for services; responsiveness and render/interaction performance for UI), designing for throughput, backpressure, and graceful degradation.

• 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
• Identity and access management experience integrating applications with enterprise IAM/SSO, including OAuth2/OIDC and/or SAML, JWT/session management, and RBAC/entitlements.
• Experience designing and implementing Multi-Factor Authentication (MFA)-aware user flows and secure session lifecycle controls (for example, re-authentication, token refresh, inactivity timeouts) aligned to enterprise security requirements.

Preferred qualifications, capabilities, and skills

• Experience building UI experiences for trading, risk, or PnL workflows in a front-office environment, with an emphasis on usability under time pressure.
• Familiarity with real-time UI delivery patterns (for example, WebSockets or streaming abstractions) and strategies to ensure data correctness, ordering, and user trust.
• Experience with UI operational excellence practices, including client-side logging/telemetry, synthetic monitoring, performance budgets, and production troubleshooting.
• Prior experience leading cross-functional delivery spanning quant/risk stakeholders, production management, and multiple engineering teams.