Job Responsibilities:

Evaluate and source appropriate cloud infrastructure solutions for machine learning needs, ensuring cost-effectiveness and scalability based on project requirements.
Automate and manage the deployment of machine learning models into production environments, ensuring version control for models and datasets using tools like Docker and Kubernetes.
Set up monitoring tools to track model performance and data drift, conduct regular maintenance, and implement updates for production models.
Work closely with data scientists, software engineers, and stakeholders to align on project goals, facilitate knowledge sharing, and communicate findings and updates to cross-functional teams.
Design, implement, and maintain scalable ML infrastructure, optimizing cloud and on-premise resources for training and inference.
Document ML processes, pipelines, and best practices while preparing reports on model performance, resource utilization, and system issues.
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