Lead Software Engineer - Fullstack Lead

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
• 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.
• 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
• 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
• 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.