AI Infrastructure Specialist

20 hours ago

Las Piñas, National Capital Region, Philippines beBeeCapacity Full time $160,000 - $200,000
Job Title: Senior AI Capacity Engineer

This is a key role that oversees the creation, update, and deletion of model pools. Additionally, it ensures accurate provisioning and decommissioning across development, testing, and production environments.


Key Responsibilities:
  • Model Pool Management: Create, update, and delete model pools to optimize resource utilization.
  • Provisioning and Decommissioning: Ensure accurate provisioning and decommissioning of model pools across different environments.
  • Security Patch Management: Implement and maintain timely security patches for all model pools.
  • Compliance: Ensure compliance in regulated environments, including government clouds and internal services.
  • Quota Monitoring and Dynamic Scaling: Continuously monitor model pool usage metrics and system performance. Adjust quotas based on business demand and AI inference load to optimize resource allocation.
  • Automation and Process Improvement: Develop and maintain automated workflows for lifecycle operations and monitoring. Collaborate with cross-functional teams to improve operational efficiency and governance.

Required Skills and Qualifications:
  • C# and .NET Expertise: Strong expertise in C# and .NET for automation and service development.
  • Geneva Actions Experience: Hands-on experience with Geneva Actions for resource lifecycle automation.
  • Azure Data Explorer Knowledge: Experience with Azure Data Explorer and Kusto Query Language (KQL) and Dashboard for monitoring and analytics.
  • Azure DevOps Familiarity: Familiarity with Azure DevOps for CI/CD pipeline implementation.
  • Development Environment Proficiency: Proficient with Visual Studio Code or similar development environments.
  • Cloud Expertise: Solid understanding of model lifecycle, scaling, and quota management in cloud environments.
  • Security Patch Management Experience: Experience in security patch management in regulated or government cloud environments.
  • Problem-Solving Skills: Strong problem-solving skills and ability to work in fast-paced, dynamic environments.
  • Senior Engineers: Senior engineers must have at least 5 years of relevant experience.

Preferred Qualifications:
  • Ai/ML Model Deployment Experience: Experience with Ai/ML model deployment and inference workloads.
  • Large-Scale Cloud Platforms Experience: Experience working with large-scale cloud platforms such as Azure.
  • Performance Tuning and Cost Optimization Understanding: Understanding of performance tuning and cost optimization for cloud-based ML services.

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