As an Agentic AI Full Stack Software Engineer III- Java/Python/React at JPMorganChase within the Asset and Wealth Management Technology Team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Design, develop, test, and deploy external client-facing web applications that embed Generative AI and Agentic AI capabilities to enhance automation, personalization, and decision-making
- Build React-based UI experiences for LLM-powered workflows (e.g., chat, search, document Q&A, summarization, assisted advisory), emphasizing usability, accessibility, and performance
- Implement and integrate agentic AI workflows that support multi-step task execution, tool/API orchestration, and defined human-in-the-loop controls and guardrails
- Develop and maintain front-end architecture (component patterns, state management, routing, error boundaries) and collaborate on UX patterns for AI features (streaming responses, citations, traceability, retry/fallback flows)
- Implement Model Context Protocol (MCP) integrations to enable AI agents to securely connect to and interact with external data sources, APIs, and enterprise tools in real time
- Build and maintain scalable data pipelines and data processing workflows for both structured and unstructured data, leveraging cloud services to support LLM-based features and real-time client interactions
- Design and develop robust APIs and microservices to integrate AI/LLM models into client-facing platforms, ensuring seamless, low-latency experiences
- Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness
- Apply 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
- Implement observability, monitoring, and feedback loops for agentic AI systems to track agent behavior, detect hallucinations, and ensure reliability in high-stakes financial applications; Gather, analyze, synthesize, and develop visualizations and reporting from large, diverse datasets to inform product improvements, monitor model performance, and enhance client outcomes
- Partner closely with product, design, and business stakeholders to translate client needs and business requirements into scalable AI-driven technical solutions; Ensure all client-facing solutions adhere to strict security, privacy, and regulatory standards applicable to the financial services industry, including data protection, model governance, and responsible AI practices, particularly as regulators increase scrutiny of autonomous AI agents in financial services
Required qualifications, capabilities, and skills
- Formal training or certification in software engineering concepts with 3+ years of applied experience
- Proficiency in Java and/or Python, with strong fundamentals in data structures, object-oriented design, and API design
- Experience building external-facing web applications with React, TypeScript/JavaScript, HTML, and CSS, and a modern state-management approach (e.g., Redux, Zustand, React Query, or Context API)
- Experience developing back-end services, RESTful APIs, and microservices using frameworks such as Spring Boot (Java) or FastAPI/Flask (Python)
- Experience designing for scalability and resiliency, including fault tolerance and high availability
- Hands-on experience integrating NLP, LLM, or Generative AI models into production software applications
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices
- Hands-on experience with cloud platforms (AWS or Azure) for AI/ML model deployment, data processing, and infrastructure management
- Working knowledge of application security best practices for client-facing financial applications, including authentication and authorization, secure session/identity management, data encryption, access controls, and common vulnerability mitigation
- Proficiency working with databases and data querying (SQL or equivalent)
Preferred qualifications, capabilities, and skills
- Financial services industry experience