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About the role
About the Role We are looking for a Senior Cloud Engineer with 5–8 years of experience to build and scale cloud-native ML infrastructure and deployment platforms across Azure, Databricks, Kubernetes, and MLOps environments. The ideal candidate should have strong hands-on experience in cloud application development, Kubernetes-based deployments, automation, and platform engineering — not just operational DevOps support. We are specifically looking for candidates with deep expertise in cloud-native architecture, infrastructure automation, and scalable ML deployment systems. Work from Office – Minimum 4 Days/Week Immediate Joiners or Candidates with Notice Period up to 15 Days Preferred Tech Stack Azure | Databricks | AKS | ARO | Terraform | MLflow | CI/CD Key Responsibilities Build and maintain CI/CD/CT pipelines for ML and cloud-native applications Develop scalable deployment workflows for MLflow models, Databricks jobs, and microservices on AKS/ARO Build cloud-native infrastructure and deployment automation using Terraform and GitOps practices Design and manage Kubernetes-based application deployment environments Develop automation scripts and platform tooling using Python, Bash, or PowerShell Optimize Databricks, AKS clusters, networking, and model serving environments Implement monitoring, logging, alerting, security, and governance for MLOps platforms Collaborate with ML Engineers, Data Engineers, and Application teams for production deployments Required Skills Strong hands-on experience with Azure cloud services and cloud-native development Experience with AKS, ARO, Kubernetes, and scalable application deployments Strong understanding of cloud architecture and distributed systems Experience building CI/CD pipelines using Azure DevOps, GitHub Actions, or Jenkins Hands-on experience with Databricks and MLflow deployments Strong Terraform and infrastructure automation experience Proficiency in Python and Bash/PowerShell scripting Good understanding of cloud security, networking, and governance Good to Have Experience with GitOps and Infrastructure as Code practices Exposure to model serving and MLOps platforms Experience building scalable internal developer platforms or cloud tooling
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