React

JPMorganChase•Jersey City, NJ

21h

About The Position

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Software Engineer III- Big Data / AI/ML / Python / React at JPMorganChase within the Asset and Wealth Management Technology AI 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. Come join the Global Asset Wealth Management Client Technology AI Team delivering production quality AI solutions to multiple lines of business in Asset Management. In this role you will be designing solutions in environments where trust and explainability are critical. You will Collaborate across business and tech, which is core to our culture, and we value people who can connect the dots across disciplines to help bring AI to life inside production systems.

Requirements

Formal training or certification on software engineering concepts and 3+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability
Experience building and deploying full stack applications in production environments
Experience developing multi model LLM systems
Proficiency in modern web development frameworks (e.g., React, Angular, Vue) and back-end technologies (e.g., Node.js, Python, Java, .NET)
Experience integrating AI/ML models into applications (e.g., using REST APIs, microservices, or cloud-based ML platforms)
Familiar with Python-based ML libraries (e.g., Scikit-learn, PyTorch, TensorFlow) and ability to work with data scientists to operationalize models
Strong understanding of software engineering best practices (version control, testing, CI/CD, code reviews)
Excellent communication skills; able to explain technical concepts to non-technical stakeholders
Comfortable working in ambiguous problem spaces and collaborating across technical and business domains
Detail-oriented and mission-driven; focused on delivering solutions that work in production

Nice To Haves

Experience in financial services or other regulated industries
Familiarity with model monitoring, explainability, or AI governance in production environments
Experience with multi-agent systems or chaining AI models with traditional systems
Interest in responsible AI practices and working with compliance/legal teams

Responsibilities

Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
Designs, develops, and deploys scalable web applications and services that integrate AI/ML models to solve real-world banking problems (e.g., document understanding, workflow automation).
Collaborates with data scientists and machine learning engineers to productionize AI solutions, ensuring seamless integration into business workflows.
Engages with stakeholders to understand business requirements, frame technical problems, and deliver user-friendly solutions.
Contributes to best practices in software engineering, including code quality, testing, CI/CD, and responsible AI deployment.
Works across the technology stack (front-end, back-end, cloud, APIs, databases) to deliver robust, maintainable solutions.
Partners with cross-functional teams such as Product Management, Technology, Legal, Compliance, and Business Management to deploy solutions into production
Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture