Senior ML Ops Engineer

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.
• Provide training and support for team members on ML Ops tools and methodologies, and stay updated on industry trends and emerging technologies.
• Diagnose and resolve issues related to model performance, infrastructure, and data quality, implementing solutions to enhance model robustness and reliability.

Education, Technical Skills & Other Critical Requirement :

Skills Required

Airflow, Google Cloud Platform, Prometheus, Elk Stack, Grafana, Sql, Nosql, Tensorflow, Azure ML, Jenkins, Git, Pytorch, Docker, Azure, Python, Kubernetes, Aws