MLOps Engineer 36 s

What you'll work on

MLOps Implementation: Design and implement MLOps pipelines to automate and streamline the deployment, monitoring, and management of machine learning models. Ensure the models are scalable, reproducible, and maintainable.

• Infrastructure Management: Manage and optimize infrastructure using Terraform and cloud platforms (e.g., AWS, Azure, GCP) to support machine learning workloads. Automate infrastructure provisioning and configuration using tools like
• CI/CD Pipelines: Develop and maintain CI/CD pipelines tailored for machine learning
• Containerization and Orchestration: Use Docker for containerizing machine learning applications and Kubernetes for orchestrating and managing containerized
• Monitoring and Performance: Implement monitoring solutions for machine learning models and infrastructure. Track model performance, detect anomalies, and ensure system
• Collaboration: Work closely with data scientists to understand model requirements and deployment needs. Provide guidance on best practices for model integration and
• Documentation: Maintain thorough documentation for MLOps workflows, infrastructure setups, and deployment

What we're looking for

• Bachelor’s Degree in Computer Science, Engineering, Data Science, or a related
• Certifications: Relevant certifications in cloud platforms (e.g., AWS Certified DevOps Engineer) and Kubernetes.
• Experience with Machine Learning Frameworks: Proficiency with machine learning frameworks and deployments