Senior Cloud Engineer – Azure & MLOps Platform

About the Role

We are looking for a Senior Cloud Engineer with 58 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