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Provides L3/L4 operational support and engineering for Citrix and Azure Virtual Desktop environments. Responsibilities include troubleshooting, platform administration, endpoint and identity management, patching, monitoring, disaster recovery, automation with Infrastructure as Code, performance and cost optimization, client advisory, documentation, incident response, and mentoring. The role supports multiple enterprise customer environments while improving reliability, security, governance, and service delivery.

Provides L3 operational support, troubleshooting, optimization, automation, security, and modernization for customer Azure environments. Manages Azure infrastructure, networking, identity, monitoring, governance, disaster recovery, and infrastructure as code. Serves as a client technical advisor, supports incident response and managed services onboarding, develops runbooks, and drives FinOps cost optimization and reliability improvements across multiple production environments.

Designs and sells complex managed services solutions while developing scalable service offerings, pricing models, documentation, standards, and enablement assets. Leads technical discovery, solution architecture, proposals, negotiations, deal governance, vendor alignment, and presales-to-delivery handoffs. Partners with Sales, Finance, Legal, Engineering, Operations, Delivery, clients, and technology providers to ensure profitable, contractually sound, and operationally ready services across cloud, infrastructure, security, end-user computing, data protection, service management, and AI-enabled technologies.

Lead end-to-end Azure infrastructure engagements: design, implement, validate, document, and transition environments to managed services. Use Cloud Adoption Framework and Well-Architected principles, perform migrations with Azure Migrate, ensure security, resiliency, operational readiness, automate via IaC, troubleshoot complex issues, and enable knowledge transfer for managed services.

Lead end-to-end architecture and delivery of production ML and generative AI platforms on Azure and Microsoft Fabric. Define MLOps, CI/CD, governance, observability, retrieval/LLM patterns, security, cost controls, and operational handoff. Work with data scientists and engineers to translate requirements, defend tradeoffs, and ensure systems run unattended in production.