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.<br><br>Responsibilities:<br>• Create and support Azure-based solutions spanning infrastructure, connectivity, identity, storage, and managed platform services in line with enterprise architecture and security expectations.<br>• Develop and optimize deployment automation using infrastructure-as-code and modern release pipelines through tools such as Azure DevOps, GitHub Actions, Bicep, and Terraform.<br>• 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.<br>• Establish and govern secure integrations for Model Context Protocol and connected enterprise systems by applying strong authentication, access controls, and data protection standards.<br>• Manage APIs, event-driven components, and security gateway configurations that enable AI services and automated workflows to connect with business applications reliably.<br>• Build retrieval-augmented AI solutions using Azure AI Search, embeddings, and vector-based data patterns to improve access to enterprise knowledge.<br>• Maintain and enhance automation solutions built with Power Automate, Logic Apps, and Copilot Studio, supporting integrations with Microsoft 365, Teams, SharePoint, and ServiceNow.<br>• Strengthen cloud governance through Azure Policy, tagging strategy, cost oversight, monitoring, logging, and operational observability tools such as Azure Monitor and Application Insights.<br>• Support Microsoft Fabric administration by overseeing tenant settings, workspace permissions, capacity controls, data access, and usage visibility across supported workloads.<br>• Contribute technical documentation, reusable standards, KPI/KRI reporting, change review activities, and incident or release support for cloud and AI environments.