Über
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.
Sprachkenntnisse
- English
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