Job Description:

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 Applied AI ML Lead at JPMorgan Chase within the Accelerator, 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 and agents

• 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
• 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
• Demonstrate proficiency in Java and/or Python programming languages
• Deploy production systems to GenAI platforms such as Google VertexAI, OpenAI, AWS Bedrock, or Lang Chain
• Utilize cloud technologies (AWS/Azure/GCP), distributed systems, CI/CD tools, infrastructure-as-code tools, and containerization/orchestration tools (Docker, Kubernetes) to operate, support, and secure mission-critical applications

Preferred qualifications, capabilities and skills

• Experience with MLOps tools and platforms such as MLflow, Amazon Sage Maker, Google VertexAI, Databricks, BentoML, KServe, and Kubeflow

• Exposure to cloud-native microservices architecture

• Familiarity with advanced AI/ML concepts and protocols, including Retrieval-Augmented Generation (RAG), agentic system architectures, and Model Context Protocol (MCP)

• Exposure to vector stores such as Pinecone, GCP RAG engine, and AWS S3 Vector Buckets

• Previous experience deploying and managing ML models

• Experience working in highly regulated environments or industries

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