React

JPMorgan Chase & Co.•Jersey City, NJ

12h

About The Position

As a 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.

Requirements

Formal training or certification in software engineering concepts with 3+ years of applied experience
Experience building external-facing web applications using Python / SpringBoot services, React JS, TypeScript/JavaScript, HTML, CSS and Redux
Hands-on practical experience in design, application development, testing, and production operational stability (monitoring, incident triage, performance tuning)
Proficient in coding in Java and/or Python with strong fundamentals (data structures, OOP, API design)
Experience developing RESTful APIs and microservices and integrating NLP or LLM models into production software applications
Hands-on experience with cloud platforms (AWS or Azure) for AI/ML model deployment, data processing, and infrastructure management
Knowledge of security best practices, particularly in the context of client-facing financial applications (e.g., data encryption, access controls, regulatory compliance)
Experience developing, debugging, and maintaining code in a large corporate environment, including working with database querying (SQL or equivalent)

Nice To Haves

Financial services industry experience

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
Develop secure, high-quality, production-grade code that powers client-facing applications, ensuring reliability, performance, and compliance with financial industry standards
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
Proactively identify hidden problems and patterns in data and system behavior, using these insights to drive improvements to code quality, system resilience, and client experience
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