Job Description: Principal Software Engineer - Applied AI ML Director

Out of the successful launch of Chase in 2021, we’re a new team, with a new mission. We’re creating products that solve real world problems and put customers at the center - all in an environment that nurtures skills and helps you realize your potential. Our team is key to our success. We’re people-first. We value collaboration, curiosity and commitment.

As a Principal Software Engineer - Applied AI ML Director at JPMorganChase within the Accelerator Business, you are the heart of this venture, focused on getting smart ideas into the hands of our customers. You have a curious mindset, thrive in collaborative squads, and are passionate about new technology. By your nature, you are also solution-oriented, commercially savvy and have a head for fintech. You thrive in working in tribes and squads that focus on specific products and projects – and depending on your strengths and interests, you'll have the opportunity to move between them.

While we’re looking for professional skills, culture is just as important to us. We understand that everyone's unique – and that diversity of thought, experience and background is what makes a good team, great. By bringing people with different points of view together, we can represent everyone and truly reflect the communities we serve. This way, there's scope for you to make a huge difference – on us as a company, and on our clients and business partners around the world.

Job Responsibilities

• Design and develop scalable, self-service solutions for documentation, SDKs, configurations and pipelines to enable rapid deployment of GenAI applications (including Retrieval-Augmented Generation (RAG) pipelines) and agents with planning, memory, and workflow orchestration.
• Implement tools and frameworks for model versioning, experiment tracking, and lifecycle management.
• Develop systems to monitor model performance and address data and model drift.
• Recommend best practices for model integration and deployment patterns.
• Design and implement effective testing strategies, including unit, component, integration, end-to-end, performance, and champion/challenger tests, establish output validation best practices, recommendations and guardrails to reduce hallucinations.
• Ensure platform compliance with data privacy, security, and regulatory standards.
• Mentor team members on platform design principles and best practices.
• Guide colleagues on coding practices, design principles, and implementation patterns for high-quality, maintainable solutions.
• Deploy scalable AI services to cloud infrastructure, ensuring monitoring, and observability for agent performance.
• Design microservices-based architectures and orchestrate multi-step workflows; instrument agents for tracing, metrics, and feedback loops to continuously improve reliability and utility.