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 Lead MLOps Platform Engineer 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 a scalable ML platform to support model training, deployment, and monitoring Build and maintain infrastructure for automated ML pipelines, ensuring reliability and reproducibility supporting different model frameworks and architectures Implement tools and frameworks for model versioning, experiment tracking, and lifecycle management Develop systems for monitoring model performance, addressing data drift and model drift Collaborate with data scientists and engineers to devise model integration/deployment patterns and best practices Optimize resource utilization for training and inference workloads Designing and implementing a framework for effective tests strategies (unit, component, integration, end-to-end, performance, champion/challenger, etc) Ensure platform compliance with data privacy, security, and regulatory standards
Mentor team members on platform design principles and best practices Mentor other team members on coding practices, design principles, and implementation patterns that lead to high-quality maintainable solutions

Required qualifications, capabilities and skills

Proficiency in coding in recent versions of Java and/or Python programming languages Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, Kubeflow)  Experience with cloud technologies (AWS/Azure/GCP) and distributed systems, web technologies and event drive architectures Understanding of data versioning and ML models lifecycle management Hands-on experience with CI/CD tools (e.g., Jenkins, GitHub Actions, GitLab CI) Knowledge of infrastructure-as-code tools (e.g., Terraform, Ansible) Strong knowledge of containerization and orchestration tools (e.g. Docker, Kubernetes) Proficiency in operating, supporting, and securing mission critical software applications

Preferred qualifications, capabilities and skills

Exposure to cloud-native microservices architecture Familiarity with advanced AI/ML concepts and protocols, such as Retrieval-Augmented Generation (RAG), agentic system architectures, and Model Context Protocol (MCP) Familiarity with model serving frameworks (e.g., TensorFlow Serving, FastAPI) Exposure to feature stores (Feast, Databricks, Hopswork, SageMaker, VertexAI) Previous experience deploying & managing ML models is beneficial Experience working in a highly regulated environment or industry

 

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