zorba ai
Website:
zorbaconsulting.in
Job details:
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
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