devops engineer for MLOps

Описания нет

• Design, develop, and implement MLOps pipelines for continuous deployment and integration of ML models
• Collaborate with data scientists to understand model requirements and optimize deployment processes
• Automate training, testing, and deployment processes for machine learning models
• Monitor and maintain models to ensure optimal performance, accuracy, and reliability
• Implement best practices for version control, model reproducibility, and governance
• Optimize machine learning pipelines for scalability, efficiency, and cost-effectiveness
• Troubleshoot and resolve issues related to model deployment and performance
• Ensure compliance with security and data privacy standards in MLOps activities
• Stay up to date with the latest MLOps tools, technologies, and trends
• Provide support and guidance to team members on MLOps practices and performance feedback

• Bachelor's degree in Computer Science, Data Science, or a related field
• 3+ Years of experience in MLOps, DevOps, or a related field
• Understanding of machine learning principles and model lifecycle management
• Proficiency in Python and hands-on experience with TensorFlow, PyTorch, or Scikit-learn
• Knowledge of CI/CD pipelines, automation tools, and version control systems such as Git
• Strong problem-solving skills and ability to troubleshoot complex issues
• Experience with monitoring tools and practices for model performance in production
• Ability to work collaboratively in cross-functional teams
• Experience with Google Cloud Platform and its machine learning services
• Familiarity with containerization and orchestration tools such as Composer and Kubernetes
• Knowledge of cloud data platform architecture, infrastructure, maintenance, and optimization

• Условий в вакансии нет