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Define and drive enterprise architecture roadmaps, cloud strategies, and target-state architectures to achieve measurable business outcomes 2. Design and govern scalable cloud and hybrid infrastructure solutions, ensuring robust architecture and operational excellence across Azure, AWS, and GCP 3. Develop and maintain reference architectures, landing zones, modernization frameworks, and solution blueprints that accelerate enterprise transformation 4. Advise CXOs and senior stakeholders on technology transformation initiatives, responsible AI adoption, and modernization strategies 5. Establish standards, best practices, governance frameworks, and reusable solution accelerators within the Cloud, AI & Infrastructure Center of Excellence 6. Lead cloud migration, modernization, and platform engineering programs, driving adoption of cloud-native architectures, automation, DevOps, containers, and infrastructure-as-code practices 7. Implement cloud governance, security, compliance, and FinOps strategies to ensure solutions are secure, scalable, and cost optimized 8. Design and establish resiliency patterns for mission-critical AI workloads, including business continuity, operational readiness, and controls for model and provider failure, with clear metrics for reliability and performance Required Skills: 1. Enterprise architecture methodologies and frameworks (e.g., TOGAF, Zachman) 2. Expert-level experience with Azure, AWS, and GCP cloud platforms 3. Cloud migration, modernization, and platform engineering expertise 4. Hands-on experience with infrastructure-as-code tools such as Terraform and Azure Resource Manager 5. Cloud-native architectures, containerization (e.g., Kubernetes, Docker), and automation frameworks 6. Cloud governance, security, compliance, and FinOps strategies 7. Designing AI-enabled infrastructure solutions and operational resilience patterns 8. Developing solution blueprints, reference architectures, and modernization frameworks 9. Establishing observability, operational readiness, and controls for AI workloads Preferred Skills: 1. Multi-cloud integration and provider-independent architecture patterns 2. Advanced AI operational controls including model drift, auditability, and human-in-the-loop safeguards 3. Business continuity and disaster recovery frameworks for AI workloads 4. Developing reusable IP assets and solution accelerators 5. Cloud platform engineering for large-scale enterprise transformations Desired Qualifications: 1. Bachelor's degree in Computer Science, Information Technology, Engineering, or a closely related discipline 2. AWS Certified Solutions Architect, Azure Solutions Architect, or Google Professional Cloud Architect certification 3. 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