Software Engineer II_AI/ML

JPMorgan Chase & Co.•New York, NY

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 II (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.

Requirements

Formal training or certification on software engineering concepts and 2+ 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

Nice To Haves

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

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.