Multi-Cloud Architect (AWS/Google Cloud Platform)

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Job Title: Multi-Cloud Architect (AWS/Google Cloud Platform)

Location: West Palm Beach, FL

Visa: H1B, USC

Job Summary:

We are looking for an experienced Multi-Cloud Architect with strong expertise in AWS and Google Cloud Platform environments to lead enterprise cloud modernization, AI enablement, data integration, governance, and infrastructure transformation initiatives. The ideal candidate should possess both strategic architecture capabilities and hands-on implementation experience across multi-cloud ecosystems.

Key Responsibilities:

• Design and implement scalable cloud-agnostic architecture solutions across AWS and Google Cloud Platform platforms
• Develop enterprise architecture standards, governance frameworks, reusable templates, and best practices
• Build proof-of-concept (POC) solutions and optimize enterprise-scale data pipelines
• Integrate enterprise tools including ServiceNow, Apptio, Jira, and cloud-native services
• Implement AI-enabled workflows and support MLOps lifecycle management
• Architect scalable structured and unstructured data processing pipelines
• Drive cloud cost optimization, AI governance, platform reliability, and operational efficiency
• Conduct architecture reviews and provide technical leadership to delivery teams
• Support cloud modernization and enterprise digital transformation initiatives

Required Skills:

• 15+ years of experience in Cloud Architecture, Infrastructure Engineering, or Data Engineering
• Strong hands-on experience with AWS and Google Cloud Platform multi-cloud environments
• Expertise in Python, SQL, Terraform, ETL/ELT pipelines, and automation
• Experience with Kubernetes and container-based architectures
• Strong understanding of AI/ML integration and MLOps concepts
• Experience designing scalable enterprise cloud and data solutions
• Strong problem-solving, architecture governance, and leadership skills

Preferred Skills:

• Experience with ServiceNow CMDB/APM integrations
• Exposure to Apptio and FinOps methodologies
• Experience handling data lineage, governance, and duplication challenges
• Exposure to Generative AI implementation projects

Success Metrics:

• Reduction in cloud cost per application
• Improved pipeline reliability and SLA performance
• Increased adoption of AI-enabled workflows
• Successful conversion of POCs into production deployments