Senior AI Capacity Engineer(India/Philippines – Remote(night shift))
5 days ago
Key Responsibilities
• Manage Model Pools with Geneva Actions
o Own the creation, updating, and deletion of model pools.
o Ensure accurate provisioning and decommissioning across Dev, Test, and Production
environments.
• Security Patch Management
o Implement and maintain timely security patches for all model pools.
o Ensure compliance in regulated environments, including US Government clouds and Microsoft
internal services.
• Quota Monitoring & Dynamic Scaling
o Continuously monitor model pool usage metrics and system performance.
o Adjust quotas based on business demand and AI inference load to optimize resource allocation
.
• Automation & Process Improvement
o Build and maintain automated workflows for lifecycle operations and monitoring.
o Collaborate with cross-functional teams to improve operational efficiency and governance.
• Monitoring & Reporting
o Analyze logs and metrics to detect anomalies, performance issues, or compliance gaps.
o Generate actionable insights to inform scaling, patching, and lifecycle decisions.
Required Skills & Experience
• Strong proficiency in C# and .NET for automation and service development.
• Hands-on experience with Geneva Actions for resource lifecycle automation.
• Experience with Azure Data Explorer and Kusto Query Language (KQL) and Dashborad for monitoring and analytics.
• Familiarity with Azure DevOps for CI/CD pipeline implementation.
• Comfortable with Visual Studio Code or similar development environments.
• Solid understanding of model lifecycle, scaling, and quota management in cloud environments.
• Experience in security patch management in regulated or government cloud environments.
• Strong problem-solving skills and ability to work in fast-paced, dynamic environments.
• Senior engineers must have at least 5 years of relevant experience
Preferred Qualifications
• Experience with AI/ML model deployment and inference workloads.
• Experience working with large-scale cloud platforms such as Azure.
• Understanding of performance tuning and cost optimization for cloud-based ML services.
Tools & Technologies
• Programming & Frameworks: C#, .NET
• Cloud & Data: Azure Data Explorer, KQL, Azure DevOps,Dashboard
• Development Tools: Visual Studio Code
• Automation & Lifecycle Management: Geneva Actions
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