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
