AI Platform Operations Engineer

Datamatics Global Service; Saudi Arabia; On-site; SAR 240,000 - 360,000; Full time

Job summary

Datamatics Global Service in Saudi Arabia seeks an AI Platform Operations Engineer to manage and govern enterprise AI platforms on Microsoft Azure, ensuring secure, scalable onboarding and operation of Generative AI workloads.

You will optimize platform services (Azure AI Foundry, Azure OpenAI), implement guardrails, monitor health, and coordinate with Microsoft, Client IT, CIS, and Data & AI teams to keep deployments compliant and performant.

Qualifications

• Hands-on Azure administration for production cloud environments.
• Deep knowledge of Azure AI Foundry / Azure OpenAI workloads.
• Experience with APIM exposure patterns and governance gates.
• Understanding of Generative AI, LLMs, and guardrails.
• Experience with observability, monitoring, and cost attribution.

Responsibilities

• Operate Azure AI platform services and related platform-level services.
• Support onboarding of GenAI workloads using landing-zone patterns.
• Assist AI gateway/APIM exposure and production readiness checks.
• Ensure on-boarding with guardrails, safety controls, and governance.</n
• Monitor AI workloads with dashboards, alerts, and health indicators.
• Coordinate with Microsoft, Client IT, CIS, and teams for incidents and risks.
• Maintain onboarding checklists, runbooks, and knowledge transfer materials.
• Track incidents, onboarding issues, and dependencies across teams.
• Support data products, event streams, and operational data stores where applicable.

Skills

Azure admin

Azure AI Foundry

APIM exposure

GenAI concepts

Observability

RBAC security

Cost governance

Tools

Azure Monitor

Log Analytics

Application Insights

Cosmos DB

ADLS Gen2

Azure Cost Management

Job description

AI Platform Operations Engineer

Experience Required: Min 3 Years

Full-Time Job

Role Summary

Responsible for the operational management, governance, and support of enterprise AI platforms, ensuring secure, scalable, and cost-effective onboarding and operation of Generative AI and Agentic AI workloads on Microsoft Azure.

Key Responsibilities

• Operate Azure AI platform services, including Azure AI Foundry / Azure OpenAI and associated platform-level services.
• Support onboarding of Nexus AI, GenAI and agentic workloads using approved landing-zone patterns, platform blueprints, governance gates and release processes.
• Support AI gateway / LLM gateway and APIM exposure, including API connectivity, registration and production-readiness checks.
• Assist use-case teams with environment readiness, identity/access, network/API connectivity, deployment pre-checks and post-deployment verification.
• Ensure AI workloads and agents are onboarded with approved guardrails, content-safety controls, observability, quota controls, cost attribution and use-case governance.
• Support prompt/model monitoring, evaluation awareness and AI observability; help validate dashboards, alerts and operational health indicators.
• Support integrations with MCP/agent interfaces, data products, event streams and operational data stores where applicable.
• Track incidents, onboarding issues, risks and dependencies; coordinate resolution with Microsoft, Client IT, CIS, Architecture, Data & AI and use-case teams.
• Maintain onboarding checklists, AI operational procedures, troubleshooting guides, governance evidence and knowledge-transfer/handover materials.

Must-Have Skills

• Minimum 3+ years of hands-on Microsoft Azure experience.
• Strong experience with Azure AI Foundry, Azure OpenAI, and Azure AI Services.
• Experience with Azure API Management (APIM) and API exposure patterns.
• Knowledge of Generative AI, Large Language Models (LLMs), RAG, and Agentic AI concepts.
• Experience implementing AI guardrails, content filtering, and Responsible AI controls.
• Familiarity with AI observability, monitoring, logging, and performance tracking.
• Experience with Azure Monitor, Application Insights, Log Analytics, and Azure Cost Management.
• Understanding of Azure security, 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 (Azure APIM AI Gateway or similar).
• Knowledge of Prompt Flow, AI evaluations, and model benchmarking frameworks.
• Experience with LangChain, LangGraph, Semantic Kernel, or AutoGen.
• Exposure to MLOps, CI/CD pipelines, GitHub Actions, and Azure DevOps.
• Knowledge of Microsoft Purview, AI governance, and compliance frameworks.
• Experience with vector databases, Azure AI Search, and RAG architectures.
• Familiarity with Kubernetes, Container Apps, or Azure OpenAI at enterprise scale.
• Knowledge of quota planning, token consumption analysis, and FinOps practices for AI workloads.

Required Experience & Skills

• 3-10 years hands-on Azure administration/operations experience, including support of production cloud environments.
• Operational knowledge of Azure AI Foundry / Azure OpenAI, GenAI workload patterns and agentic application operations.
• Understanding of AI gateway/APIM, REST APIs, MCP awareness, guardrails, content safety, prompt/model monitoring and evaluation concepts.
• Experience with Azure monitoring/observability services such as Azure Monitor, Log Analytics and Application Insights.
• Working knowledge of identity, managed identities, RBAC, secrets management, private connectivity, security controls, quota management and cost attribution.
• Operational familiarity with APIM, Azure Event Hubs, Application Insights, Cosmos DB and ADLS Gen2.
• Strong incident/problem management, stakeholder coordination, runbook preparation and knowledge-transfer skills.

Preferred Certifications

Strongly preferred: Microsoft Azure Administrator Associate (AZ-104). Additional preferred certifications: Azure AI Engineer Associate (AI-102) and Azure Solutions Architect Expert (AZ-305). Google Cloud Associate Cloud Engineer is an advantage due to cross-cloud dependencies.

Key Deliverables

AI/use-case onboarding checklist; platform monitoring and incident register; security/governance evidence inputs; AI operational runbooks and troubleshooting guides; operational dependency records; knowledge-transfer and handover pack.