Cloud Engineer AI
eFinancialCareersOperate and improve the cloud AI platform estate across Azure AI Foundry, supporting Azure services, GenAI Hub Portal, Claude Enterprise, ChatGPT Enterprise and approved AI services. This role keeps the platform reliable, supportable, well monitored, cost-aware and easy for users and developers to consume.
Core responsibilities- Run day-to-day AI platform operations across Azure AI Foundry, GenAI Hub Portal and supporting Azure services.
- Support Azure services including App Services, Functions, Storage, Key Vault, AI Search, networking, monitoring and logging.
- Manage access, environments, onboarding support, operational triage and platform guidance for users and developers.
- Operate SaaS AI products as managed services: Claude Enterprise, ChatGPT Enterprise and approved OpenAI services.
- Support platform monitoring, incident triage, performance reporting and operational runbooks.
- Maintain platform catalogue/registry accuracy for approved models, agents, skills, MCP servers and third-party tools.
- Support cost monitoring and attribution across Azure and SaaS AI usage.
- Work closely with DevSecOps Engineer on secure deployment patterns, platform standards and release/change activity.
- Work with AI Demand & CoE Lead to test technical feasibility and identify reusable platform patterns.
- Strong Azure platform engineering experience.
- Hands-on knowledge of Azure subscriptions, identity, networking, monitoring, logging and Key Vault.
- Experience with Azure App Services, Functions, Storage, AI Search and Azure AI/Foundry-style services.
- Good understanding of cloud operations, service reliability, operational runbooks and support processes.
- Experience operating SaaS and cloud platform services for enterprise users.
- Working knowledge of ServiceNow/Jira operating models and evidence discipline.
- Awareness of cloud security, access controls, vulnerability handling and audit expectations.
- Clear written communication; able to document standards and explain issues to non-specialists.
- Has run Azure production or near-production services, not just built prototypes.
- Can explain how they diagnose access, network, Key Vault, logging or performance issues.
- Has supported enterprise users/developers and understands ticket hygiene and handoffs.#
- Can work within a managed-service model rather than waiting for task-by-task instruction
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