Found Description
Job Responsibilities
Cooperate with developers and infrastructure teams to ensure highly available and scalable production systems. Build and maintain highly available and scalable production systems with a focus on optimizing ML platforms and services. Manage core production systems, including frequent changes and updates. Monitor, troubleshoot, and rapidly resolve issues in production ML systems. Implement and manage continuous training pipelines for automated model retraining and deployment. Establish best practices for MLOps, including model versioning, experiment tracking, and deployment strategies. Provide support during off hours (nights and weekends) when necessary. Required Qualifications
5+ years of MLOps experience in production cloud-native environments. Proficient verbal and written communication in English. Proven expertise in cloud-native ML platforms (e.g., Azure ML, AWS SageMaker) to architect and manage automated continuous training and deployment pi...
Cooperate with developers and infrastructure teams to ensure highly available and scalable production systems. Build and maintain highly available and scalable production systems with a focus on optimizing ML platforms and services. Manage core production systems, including frequent changes and updates. Monitor, troubleshoot, and rapidly resolve issues in production ML systems. Implement and manage continuous training pipelines for automated model retraining and deployment. Establish best practices for MLOps, including model versioning, experiment tracking, and deployment strategies. Provide support during off hours (nights and weekends) when necessary. Required Qualifications
5+ years of MLOps experience in production cloud-native environments. Proficient verbal and written communication in English. Proven expertise in cloud-native ML platforms (e.g., Azure ML, AWS SageMaker) to architect and manage automated continuous training and deployment pi...