AI Infrastructure Expert

11 hours ago

Cavite City, Calabarzon, Philippines beBeeCapacity Full time ₱5,500,000 - ₱8,500,000
Senior AI Capacity Engineer

We are seeking a Senior AI Capacity Engineer to join our team. As a key member of our engineering organization, you will be responsible for ensuring the smooth operation of our AI infrastructure, including model pools, provisioning, and decommissioning.

Key responsibilities include:

  • Ownership of model pool creation, updating, and deletion.
  • Accurate provisioning and decommissioning across Dev, Test, and Production environments.
  • Implementation and maintenance of timely security patches for all model pools.
  • Compliance in regulated environments, including US Government clouds and Microsoft internal services.
  • Quota monitoring and dynamic scaling: Continuously monitor model pool usage metrics and system performance, adjusting quotas based on business demand and AI inference load to optimize resource allocation.
  • Automation and process improvement: Build and maintain automated workflows for lifecycle operations and monitoring. Collaborate with cross-functional teams to improve operational efficiency and governance. Analyze logs and metrics to detect anomalies, performance issues, or compliance gaps. Generate actionable insights to inform scaling, patching, and lifecycle decisions.

The ideal candidate will have strong proficiency in C# and .NET for automation and service development. Experience with Geneva Actions for resource lifecycle automation is also essential. Familiarity with Azure Data Explorer and Kusto Query Language (KQL) and Dashboard for monitoring and analytics is required. A solid understanding of model lifecycle, scaling, and quota management in cloud environments is necessary. Strong problem-solving skills and the ability to work in fast-paced, dynamic environments are also crucial.

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
Automation & Lifecycle Management: Geneva Actions

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