As a Software Engineer III (AI/Machine Learning Platform Engineer) at JPMorgan Chase within the Consumer & Community Banking (CCB) line of business, you serve as a seasoned member of an agile team focused on building, scaling, and maintaining robust machine learning platforms. You will design and deliver trusted, market-leading infrastructure and tools that empower data scientists and ML engineers to develop, deploy, and monitor models efficiently and securely. You are responsible for implementing critical technology solutions across multiple technical areas to support the firm’s business objectives and drive innovation in ML platform capabilities.

Job responsibilities

• Design, build, and maintain scalable machine learning platforms and infrastructure to support end-to-end ML workflows.
• Develop and optimize tools for model training, deployment, monitoring, and lifecycle management.
• Integrate data engineering, feature management, and model serving capabilities into unified ML platform solutions.
• Implement secure, high-quality production code for platform services, APIs, and automation pipelines.
• Collaborate with data scientists, ML engineers, and product teams to understand requirements and deliver platform features that accelerate ML development and operations.
• Ensure platform reliability, scalability, and performance through proactive monitoring, troubleshooting, and continuous improvement.
• Produce architecture and design artifacts for platform components, ensuring alignment with enterprise standards and best practices.
• Automate infrastructure provisioning, configuration, and CI/CD pipelines for ML platform services.
• Contribute to the ML platform engineering community of practice and participate in events that explore new and emerging technologies.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
  

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 3+ years applied experience 
• Hands-on experience building, deploying, and maintaining machine learning platforms or infrastructure
• Proficiency in Python and one or more ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)  
• Experience with data processing frameworks and tools (e.g., Spark, Pandas, SQL)   
• Practical experience with cloud-based ML platforms (e.g., AWS SageMaker, GCP AI Platform, Azure ML) or on-prem ML infrastructure
• Strong understanding of MLOps practices, including CI/CD for ML, model versioning, and monitoring
• Experience developing APIs and platform services for ML workflows    
• Solid knowledge of the software development life cycle and agile methodologies   
• Ability to collaborate with cross-functional teams to deliver platform solutions aligned with business objectives 
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Preferred qualifications, capabilities, and skills 

• Familiarity with Databricks for scalable data engineering and ML platform integration
• Experience working with Snowflake for cloud-based data warehousing and analytics
• Exposure to Snorkel AI for programmatic data labeling and training data management
• Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, Airflow)
• Familiarity with feature stores, model registries, and ML metadata management
• Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation)

• Experience with RESTful APIs and microservices architectures