Job Description

Key Responsibilities\r\n• At least 4 to 6 years of relevant experience in MLOps team with 2-3 years hands-on \r\nexperience in supporting Azure MLOPS cloud technologies\r\n• Design, implement, and manage scalable machine learning (ML) pipelines using \r\nAzure ML, Databricks, and PySpark.\r\n• Build and maintain automated CI/CD pipelines with Bitbucket and Jenkins, \r\nincorporating SonarQube to ensure code quality and security standards.\r\n• Utilize Azure Kubernetes Service (AKS) to containerize and deploy machine learning \r\nmodels, ensuring high availability and scalability.\r\n• Develop reusable templates for various ML use cases to streamline the model \r\ndeployment process and enhance operational efficiency.\r\n• Design and manage APIs to facilitate seamless interaction between ML models and \r\nother applications, ensuring robust, secure, and scalable API interfaces.\r\n• Perform model optimization, monitoring for data drift, data refresh checks, and \r\nensure the ML pipelines are cost-efficient.\r\n• Implement cost monitoring and management strategies to ensure efficient use of \r\nresources, particularly for model training and deployment phases.\r\n• Work closely with data scientists, DevOps, and IT teams to deploy and manage \r\nmachine learning models across environments.\r\n• Provide thorough documentation for ML workflows, pipeline templates, and \r\noptimization strategies to support cross-team collaboration