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: databricks,kubernetes,azure,terraform
