Senior Cloud Engineer_5+years

Role Summary

We are seeking a highly skilled Cloud / MLOps Engineer to support machine learning development teams and drive end-to-end automation for model deployment and operations. The ideal candidate will have strong expertise across Azure, Databricks, and Kubernetes platforms, with a focus on building scalable, secure, and efficient MLOps pipelines.

Key Responsibilities

• Design, build, and maintain CI/CD/CT pipelines for ML models using tools such as Azure DevOps, GitHub Actions, or Jenkins
• Develop and manage deployment workflows for:
• Databricks Jobs
• MLflow models
• Microservices deployed on AKS / ARO
• Automate infrastructure provisioning and management using Terraform, scripting, and GitOps practices
• Manage and optimize:
• Databricks workspaces
• AKS clusters and containerized workloads
• Networking and model serving environments
• Implement monitoring, logging, and alerting solutions to ensure platform reliability and performance
• Collaborate closely with ML Engineers, Data Engineers, and Application Teams to streamline deployment workflows
• Ensure adherence to security best practices, governance standards, and cost optimization strategies across MLOps pipelines

Required Skills & Qualifications

• Strong hands-on experience with Microsoft Azure, Azure Kubernetes Service (AKS), and Azure Red Hat OpenShift (ARO)
• Proven experience with Azure Databricks and distributed data processing
• Solid understanding of MLflow and Kubernetes-based model deployment strategies
• Proficiency in Python and scripting languages (Bash / PowerShell)
• Experience with Infrastructure as Code (Terraform) and CI/CD tools
• Good understanding of cloud security, networking, and distributed systems
• Experience with containerization and orchestration (Docker, Kubernetes)

Preferred Qualifications

• Experience with GitOps workflows and version-controlled deployments
• Familiarity with monitoring tools (e.g., Prometheus, Grafana, Azure Monitor)
• Knowledge of cost optimization techniques in cloud environments
• Exposure to enterprise-scale ML systems and production-grade deployments

Key Competencies

• Strong problem-solving and troubleshooting skills
• Excellent collaboration and communication abilities
• Ability to work in a fast-paced, cross-functional environment
• Focus on automation, scalability, and reliability

Skills: terraform,kubernetes,azure,databricks