We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within Asset & Wealth Management, 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:
- Design and deliver features across the full stack — backend APIs, AI agent orchestration, and frontend UI
- Build and maintain LLM tool integrations, keeping service responsibilities clean and well-bounded
- Integrate with internal and third-party financial data services, translating domain data into LLM-usable context
- Review the architectural surface of every feature before it ships
- Collaborate closely with team members — participating in design discussions, sharing context, and communicating trade-offs clearly both in writing and in conversation
- Keep documentation and inline comments current — stale docs are treated as bugs
- Adds to team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills:
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Java + Spring Boot — REST APIs, clean service boundaries, modern Java idioms
- **Strong architecture instincts **— ability to reason about service boundaries, separation of concerns, layering, and long-term maintainability
- **TypeScript + React **— strict TypeScript, state management, real component design
- AI/LLM integration — hands-on experience with agentic systems: tool calling, structured output, context management, prompt design
- **Distributed systems fundamentals **— WebSocket, SSE, REST; able to debug across multiple services
- Clear communicator — comfortable discussing technical decisions with teammates, asking the right questions, and aligning on approach before diving into implementation
Preferred qualifications, capabilities, and skills:
- Background in financial services, fintech, or wealth management — familiarity with investment products, portfolio concepts, or client data workflows is a plus
- Familiarity with MCP (Model Context Protocol) or similar LLM tool-integration patterns
- Experience with LangGraph or other graph-based agent frameworks (LangChain, AutoGen) is a plus
- Comfort operating at prototype pace — patterns still crystallizing, right abstraction not always obvious, expected to ask before guessing
- You'll join a small, high-ownership team building a next-generation AI-powered advisory platform that helps financial professionals work faster and smarter.
- This is full-stack, end-to-end work across a modern polyglot architecture: Java microservices, a Python AI agent, and a React frontend.