Azure Cloud Engineer - AI & Automation

We are looking for an Azure Cloud Engineer – AI & Automation to join a contract opportunity based in Pennsylvania. In this role, you will help shape and support secure Azure environments while enabling AI-driven capabilities across cloud infrastructure, automation, and platform operations. This position is ideal for someone who enjoys balancing hands-on engineering with governance, reliability, and scalable delivery practices in a highly regulated environment.Responsibilities:• Create and support Azure-based solutions spanning infrastructure, connectivity, identity, storage, and managed platform services in line with enterprise architecture and security expectations.• Develop and optimize deployment automation using infrastructure-as-code and modern release pipelines through tools such as Azure DevOps, GitHub Actions, Bicep, and Terraform.• Operate and administer AI services across Azure AI Foundry, Azure OpenAI, Claude, and related platforms, including model setup, permission management, policy enforcement, evaluation, and live environment support.• Establish and govern secure integrations for Model Context Protocol and connected enterprise systems by applying strong authentication, access controls, and data protection standards.• Manage APIs, event-driven components, and security gateway configurations that enable AI services and automated workflows to connect with business applications reliably.• Build retrieval-augmented AI solutions using Azure AI Search, embeddings, and vector-based data patterns to improve access to enterprise knowledge.• Maintain and enhance automation solutions built with Power Automate, Logic Apps, and Copilot Studio, supporting integrations with Microsoft 365, Teams, SharePoint, and ServiceNow.• Strengthen cloud governance through Azure Policy, tagging strategy, cost oversight, monitoring, logging, and operational observability tools such as Azure Monitor and Application Insights.• Support Microsoft Fabric administration by overseeing tenant settings, workspace permissions, capacity controls, data access, and usage visibility across supported workloads.• Contribute technical documentation, reusable standards, KPI/KRI reporting, change review activities, and incident or release support for cloud and AI environments.