Azure DevOps / AI Platform Engineer

We are seeking a hands-on Azure DevOps / AI Platform Engineer to build, automate, secure, and operate enterprise Azure platforms supporting Generative AI, cloud-native applications, data services, and Microsoft Fabric/OneLake workloads. This is a platform engineering role focused on Infrastructure as Code, CI/CD automation, DevSecOps controls, cloud networking, identity, observability, governance, and production readiness across Dev, Test, and Production environments., Engineer Azure landing-zone components, including subscriptions, resource groups, VNets, subnets, NSGs, private endpoints, private DNS, routing, peering, firewall integration, identity boundaries, naming standards, tagging, and environment isolation.

Build and maintain Terraform-based Infrastructure as Code modules and Azure DevOps YAML pipelines for repeatable provisioning, automated validation, approvals, artifact promotion, release gates, rollback, and controlled deployments across multiple environments.

Deploy and integrate Azure AI Foundry, Azure OpenAI, Azure AI Search, API Management, Key Vault, Storage, managed databases, Microsoft Fabric/OneLake, Purview, and Retrieval-Augmented Generation patterns using secure private connectivity and governed access models.

Implement DevSecOps controls, including least privilege RBAC, Managed Identity, App Registrations, OAuth/OIDC, Key Vault secret management, secure pipeline variables, SAST, dependency scanning, IaC scanning, secret scanning, vulnerability gates, and production change controls.

Operate platform observability and governance using Azure Monitor, Application Insights, Log Analytics, alerts, dashboards, Azure Policy, cost/quota monitoring, runbooks, architecture diagrams, operational documentation, and audit-ready evidence.

5+ years in Cloud Engineering, DevOps, DevSecOps, SRE, or Platform Engineering, with 3+ years of hands-on Azure delivery experience in production environments.

Strong hands-on experience with Terraform, Azure DevOps, Git, YAML pipelines, Azure networking, Entra ID, Azure RBAC, Key Vault, private endpoints, Azure Monitor, Log Analytics, and secure multi-environment deployment practices.

Demonstrated ability to troubleshoot complex cloud platform issues, work across application, cybersecurity, network, data, architecture, and governance teams, and produce clear technical documentation for production operations., Preferred experience includes Azure AI Foundry, Azure OpenAI, Azure AI Search, Microsoft Fabric/OneLake, Purview, API Management, AKS, App Services, GitOps, Azure Policy, Bicep, PowerShell, Python, containerization, enterprise AI governance, and regulated industries such as banking, financial services, healthcare, or government. Microsoft certifications such as AZ-104, AZ-305, AZ-400, AZ-500, AI-102, or AI-900 are a plus., Recruiters should prioritize candidates who can independently design and operate the Azure platform layer for AI-enabled applications, not only write application code. Strong candidates will be able to explain how they would deploy a Generative AI or RAG workload securely through Dev, Test, and Production, including network isolation, identity, IaC, CI/CD, secrets, monitoring, policy, release gates, and operational support.