AI Model Deployment Specialist

1 day ago

Taguig, National Capital Region, Philippines beBeeCloud Full time $120,000 - $150,000
Model Pool Engineer Role

We are seeking a skilled Model Pool Engineer to own the creation, updating, and deletion of model pools. This includes ensuring accurate provisioning and decommissioning across Dev, Test, and Production environments.

The ideal candidate will implement and maintain timely security patches for all model pools, ensuring compliance in regulated environments, including US Government clouds and Microsoft internal services.

They will continuously monitor model pool usage metrics and system performance, adjusting quotas based on business demand and AI inference load to optimize resource allocation.

Additionally, they will build and maintain automated workflows for lifecycle operations and monitoring, collaborating with cross-functional teams to improve operational efficiency and governance.

The successful candidate will analyze logs and metrics to detect anomalies, performance issues, or compliance gaps, generating actionable insights to inform scaling, patching, and lifecycle decisions.

Requirements
  • 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 Dashboard 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.
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.

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