AI Infrastructure Engineer (Azure AI Foundry)

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We are seeking a highly skilled AI Infrastructure Engineer with hands-on experience in Azure AI Foundry to design, deploy, and scale enterprise-grade AI platforms supporting modern GenAI and ML workloads. This role will focus on building secure, scalable, and production-ready AI infrastructure aligned with cloud-native best practices.

This opportunity is ideal for engineers experienced in Azure AI platform architecture, LLM deployment pipelines, and enterprise infrastructure automation.

 

Key Responsibilities

  • Design and implement enterprise AI infrastructure using Azure AI Foundry
  • Deploy and manage LLM-based solutions using Azure OpenAI services
  • Build scalable pipelines supporting model training, evaluation, and inference
  • Configure secure networking for AI workloads using:
    • Private Endpoints
    • VNET integrations
    • Managed identities
  • Develop infrastructure automation using:
    • Terraform
    • ARM templates
    • Bicep
  • Implement observability solutions across AI workloads using:
    • Azure Monitor
    • Log Analytics
    • Application Insights
  • Support governance frameworks including:
    • RBAC
    • Policy enforcement
    • Data security controls
  • Collaborate with data scientists and application teams to operationalize models
  • Optimize performance, reliability, and cost of AI environments

 

Required Skills & Experience

  • Strong hands-on experience with Azure AI Foundry
  • Experience deploying Azure OpenAI / GenAI workloads
  • Deep knowledge of:
    • Azure Kubernetes Service (AKS)
    • Azure Machine Learning
    • Azure Container Apps
  • Experience building production AI/ML infrastructure
  • Infrastructure-as-Code experience:
    • Terraform preferred
  • Experience supporting enterprise networking architecture
  • Strong scripting skills in:
    • Python
    • PowerShell
    • Bash
  • Experience with CI/CD pipelines:
    • Azure DevOps
    • GitHub Actions

 

Preferred Qualifications

  • Experience supporting multi-region AI deployments
  • Knowledge of LLMOps / MLOps frameworks
  • Experience implementing model governance and monitoring
  • Familiarity with vector databases and RAG pipelines
  • Exposure to secure enterprise AI gateways
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