Software Engineer II_AI/ML
at J.P. Morgan
Posted a day ago
No clicks
- Compensation
- Not specified USD
- City
- New York City
- Country
- United States
Currency: $ (USD)
As a Software Engineer II (Machine Learning Platform Engineer), you will design, build, and maintain scalable ML platforms and infrastructure to support end-to-end ML workflows. You will develop tools for model training, deployment, monitoring, and lifecycle management, and integrate data engineering, feature management, and model serving into unified platform solutions. You will write secure, high-quality production code for platform services and APIs, and collaborate with data scientists and product teams to accelerate ML development and operations, while ensuring platform reliability, scalability, and performance.
Location: NY, United States
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.
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
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
Software Engineer II_AI/ML
at J.P. Morgan
Software Engineer II_AI/ML
at J.P. Morgan
Posted a day ago
No clicks
- Compensation
- Not specified USD
- City
- New York City
- Country
- United States
Currency: $ (USD)
As a Software Engineer II (Machine Learning Platform Engineer), you will design, build, and maintain scalable ML platforms and infrastructure to support end-to-end ML workflows. You will develop tools for model training, deployment, monitoring, and lifecycle management, and integrate data engineering, feature management, and model serving into unified platform solutions. You will write secure, high-quality production code for platform services and APIs, and collaborate with data scientists and product teams to accelerate ML development and operations, while ensuring platform reliability, scalability, and performance.
Location: NY, United States
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.
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
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
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