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Metro Bangalore, popular cloud/MLOps title and broad skillset increase applicant density.
Cloud and MLOps skills are platform-specific but reasonably transferable across industries with cloud adoption.
Requires specific Azure, AKS, Databricks, MLflow and Terraform skills, making filters stringent.
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Develop and maintain CI/CD/CT pipelines for ML models using Azure DevOps, GitHub Actions, or Jenkins.
Automate infrastructure provisioning and management using Terraform, scripting, and GitOps for Azure, AKS, ARO, Databricks, and Kubernetes environments.
Implement monitoring, logging, and incident response to ensure platform reliability and optimize cost, security, and governance of MLOps pipelines.
Strong hands-on experience with Azure cloud services, Azure Kubernetes Service (AKS), and Azure Red Hat OpenShift (ARO).
Experience with Databricks and MLflow for model deployment and management.
Proficiency in Python and Bash or PowerShell scripting.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in end-to-end MLOps pipeline development and automation focusing on Azure and Kubernetes ecosystems.
Skilled at collaborating with ML engineers, data engineers, and application teams to deliver secure, scalable ML model deployment and operations.
Strong understanding of cloud security, networking, distributed systems, and cost optimization in production ML environments.