Senior AI Platform Engineer

Clurgo is a company created by developers for developers, working on diverse IT projects for clients across various industries, ensuring good programming practices and work-life balance.

• Minimum 5 years experience in Cloud Engineering with strong knowledge of Microsoft Azure, including design and maintenance of production cloud environments
• Experience building AI/ML platforms in Azure with services such as Azure AI Foundry, Azure API Management, and Azure Kubernetes Service (AKS)
• Strong knowledge of Terraform and Infrastructure as Code for automating infrastructure provisioning and configuration
• Practical experience with CI/CD tools including Azure DevOps and GitHub Actions, capable of building and maintaining deployment pipelines
• Good knowledge of Docker, Kubernetes, and containerized service deployment and scaling
• Experience with monitoring and observability in Azure, particularly Azure Monitor, Log Analytics, and Application Insights
• Understanding of cloud security, IAM, and governance, including Microsoft Entra ID, RBAC, Managed Identities, Key Vault, and secrets management
• Proficient with Python, PowerShell, Azure CLI, or similar automation tools

Nice to have:

• Familiarity with Azure Machine Learning, Azure AI Search / Cognitive Search, Azure AI Foundry Agent Service, Azure Cosmos DB, Azure Functions
• Experience with RAG, LLM-based solutions, Model Context Protocol (MCP), and Redis

• Designing, developing, and maintaining a scalable AI/ML platform in Microsoft Azure
• Building and automating cloud infrastructure using Terraform and Infrastructure as Code
• Creating and maintaining CI/CD pipelines for platform components and AI solutions
• Deploying and maintaining services in Azure Kubernetes Service and container environments
• Integrating Azure AI services, including Azure AI Foundry, with other platform components
• Designing monitoring, observability, and alerting mechanisms for the platform and deployed solutions
• Ensuring security, IAM, secrets management, and compliance with organizational standards
• Automating deployment, configuration, and maintenance processes
• Collaborating with AI/ML teams, developers, and architects to deploy AI solutions to production

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