AI Platform Operations Engineer
Riyadh, Riyadh Province, Saudi Arabia · Full Time
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- Experience
- 3–10 yrs
- Salary
- —
- Openings
- 1
- Posted
- 1 day ago
- Work mode
- In office
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Job description
Role Overview
Seeking an AI Platform Operations Engineer responsible for managing, governing, and supporting enterprise AI platforms. The role ensures secure, scalable, and cost-efficient deployment and operation of Generative and Agentic AI workloads on Microsoft Azure cloud infrastructure.
Key Duties
- Manage and operate Azure AI platform services, including Azure AI Foundry, Azure OpenAI, and related components.
- Facilitate onboarding of Nexus AI, Generative AI, and agentic workloads with established landing zone architectures, governance protocols, and release processes.
- Provide support for AI gateway/LLM gateway and API Management (APIM), including API connectivity, registration, and production readiness validation.
- Assist use-case teams with environment configuration, identity and access management, network and API connectivity, deployment validation, and post-deployment checks.
- Ensure AI workloads comply with guardrails, content safety standards, observability metrics, quota control, cost tracking, and governance policies.
- Monitor prompts and models, validate dashboards and alerts, and maintain AI operational health indicators.
- Support integration with Microsoft Cloud Platform (MCP) agent interfaces, data products, event streaming services, and operational data stores as needed.
- Track and coordinate incident management, onboarding challenges, risk mitigation, and dependencies across internal teams and Microsoft support.
- Maintain comprehensive onboarding checklists, operational procedures, troubleshooting documents, governance evidence, and knowledge transfer materials.
Required Expertise
- At least 3 years of practical experience working with Microsoft Azure.
- Strong proficiency in Azure AI Foundry, Azure OpenAI, and Azure AI services.
- Hands-on knowledge of Azure API Management (APIM) and API exposure frameworks.
- Understanding of Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI concepts.
- Experience with implementing AI guardrails, content filtering, and responsible AI controls.
- Familiarity with AI observability, including monitoring, logging, and performance analysis.
- Experience utilizing Azure Monitor, Application Insights, Log Analytics, and Azure Cost Management tools.
- Good grasp of Azure security mechanisms like RBAC, Managed Identities, Key Vault, and networking.
- Strong troubleshooting ability, operational support skills, and effective stakeholder communication.
Desirable Skills
- Exposure to AI Gateway solutions such as Azure APIM AI Gateway or equivalents.
- Knowledge of Prompt Flow, AI evaluation frameworks, and model benchmarking methodologies.
- Experience with libraries and frameworks like LangChain, LangGraph, Semantic Kernel, or AutoGen.
- Familiarity with MLOps practices, including CI/CD pipelines, GitHub Actions, and Azure DevOps.
- Understanding of Microsoft Purview, AI governance policies, and compliance standards.
- Experience with vector databases, Azure AI Search, and RAG architectures.
- Knowledge of container orchestration using Kubernetes or Azure Container Apps and managing Azure OpenAI at enterprise scale.
- Insight into quota planning, token usage monitoring, and financial operations (FinOps) for AI workloads.
Experience Required
3 to 10 years of hands-on Azure administration and operational experience, specifically supporting production cloud environments. Operational knowledge of Azure AI Foundry, Azure OpenAI, generative AI workload patterns, agentic applications, API management, security controls, and observability practices is essential.
Certifications Preferred
Strong preference for candidates certified as Microsoft Azure Administrator Associate (AZ-104). Additional desirable certifications include Azure AI Engineer Associate (AI-102), Azure Solutions Architect Expert (AZ-305), and Google Cloud Associate Cloud Engineer due to cross-cloud dependencies.
Deliverables
- Maintain AI use-case onboarding checklists.
- Manage platform monitoring and incident records.
- Provide documentation for security and governance compliance.
- Create and update operational runbooks and troubleshooting guides.
- Document operational dependencies and prepare knowledge transfer packages.