Description

Provide end-to-end operational support for Machine Learning (ML) workloads across Azure and Databricks, ensuring availability, scalability, and reliability of the Machine Learning (ML) pipelines and infrastructure.
- Manage and monitor Azure ML services, Databricks clusters, and underlying cloud infrastructure, responding to incidents and performance degradation.
- Support deployment and versioning of Machine Learning (ML) models using CI/CD pipelines, MLflow, Azure DevOps, and containerized environments.
- Troubleshoot and resolve issues across the Machine Learning (ML) lifecycle, including data ingestion, model training, model serving, and batch inference jobs.
- Implement observability practices using tools like Azure Monitor, Log Analytics, and Datadog to track system health, metrics, logs, and alerts.
- Enforce security and compliance standards across Machine Learning (ML) environments, including access control, key/certificate management, and secure data handling.
- Optimize cost and performance of ML infrastructure by managing compute resources, job scheduling, auto-scaling, and right-sizing clusters.
- Support automation of operational tasks through scripting (PowerShell, Bash, Python) for monitoring, deployment, and maintenance workflows.
- Collaborate with data scientists and Machine Learning (ML) engineers to provide technical guidance and ensure smooth integration of models into production environments.
- Maintain operational runbooks, SOPs, and incident reports to ensure structured support, knowledge transfer, and audit-readiness

Key Skills

Education

Any Graduate