What you will do:

• Work on the development and implementation of MLOps processes, and best practices for the entire machine learning lifecycle, from model training to deployment and monitoring.
• Design, build, and maintain robust, scalable, and secure ML infrastructure in the cloud (AWS), ensuring high availability and performance.
• Develop infrastructure for large-scale distributed training and experimentation.
• Collaborate closely with the backend team to integrate MLOps processes into the overall CI/CD pipeline and infrastructure.

What makes you a great fit:

• Years of experience: 3 - 6 years, in which at least 1/2 + years of experience working in MLOps with hands-on experience in deploying and maintaining machine learning models in large-scale production environments.
• Extensive experience with cloud platforms (AWS) and their machine learning services, including model deployment and monitoring.
• Deep understanding of containerization technologies (e.g., Docker, Kubernetes) and experience with container orchestration.
• Solid understanding of DevOps principles and experience with CI/CD pipelines, automated testing, and infrastructure-as-code