AI Platform Operations Engineer

Role Summary

Experience Required

• A minimum of 3 years of hands-on Microsoft Azure experience is required.
• The role requires Azure administration or operations experience supporting production cloud environments.

Platform Operations

• Operate Azure AI Foundry, Azure OpenAI, Azure AI Services, and associated platform-level services.
• Support onboarding of Nexus AI, Generative AI, and agentic workloads using approved landing zones, blueprints, governance gates, and release processes.
• Support AI gateway and Azure API Management exposure, including connectivity, registration, and production-readiness checks.

Environment and Governance Support

• Assist use-case teams with environment readiness, identity and access, network and API connectivity, deployment checks, and post-deployment verification.
• Apply approved guardrails, content-safety controls, observability, quota controls, cost attribution, and use-case governance to AI workloads and agents.
• Support integrations with MCP and agent interfaces, data products, event streams, and operational data stores where applicable.

Monitoring and Incident Management

• Support prompt and model monitoring, evaluation awareness, dashboards, alerts, and operational health indicators.
• Track incidents, onboarding issues, risks, and dependencies and coordinate resolution with Microsoft and internal technology teams.
• Maintain onboarding checklists, operational procedures, troubleshooting guides, governance evidence, and knowledge-transfer materials.

Must-Have Skills

• Strong hands-on experience with Microsoft Azure, Azure AI Foundry, Azure OpenAI, and Azure AI Services.
• Experience with Azure API Management, Generative AI, Large Language Models, RAG, and Agentic AI concepts.
• Experience implementing AI guardrails, content filtering, Responsible AI controls, monitoring, logging, and performance tracking.
• Knowledge of Azure Monitor, Application Insights, Log Analytics, Azure Cost Management, RBAC, Managed Identities, Key Vault, and networking concepts.
• Strong troubleshooting, operational support, and stakeholder management skills.

Good-to-Have Skills

• Experience with AI Gateway solutions, Prompt Flow, AI evaluations, model benchmarking frameworks, LangChain, LangGraph, Semantic Kernel, or AutoGen.
• Exposure to MLOps, CI/CD pipelines, GitHub Actions, Azure DevOps, Microsoft Purview, governance frameworks, vector databases, Azure AI Search, Kubernetes, or Container Apps.
• Knowledge of quota planning, token consumption analysis, and FinOps practices for AI workloads.

Preferred Certifications

• Microsoft Azure Administrator Associate AZ-104 is strongly preferred.
• Azure AI Engineer Associate AI-102 and Azure Solutions Architect Expert AZ-305 are additionally preferred.
• Google Cloud Associate Cloud Engineer is advantageous because of cross-cloud dependencies.

Key Deliverables