AI Architect: Cloud Modernization & GenAI Pioneer

Aimlroles; United States; On-site; USD 180,000 - 280,000; Full time

Job summary

Aimlroles is seeking a seasoned software architect to lead enterprise cloud modernization across Azure and Google Cloud. You will design scalable AI-enabled platforms, modernize monolithic apps into microservices, and govern security and DevSecOps practices.

Responsibilities span architecture, integration, data strategy, and AI governance, with a focus on reliability, performance, and cost optimization in large-scale deployments.

Qualifications

• 10+ years of experience in software engineering, architecture, and enterprise application delivery.
• 5+ years of hands-on Azure/Google cloud architecture experience.
• 3+ years of experience designing and implementing AI/Generative AI solutions.
• Proven success leading large-scale application modernization and cloud transformation initiatives.
• Experience delivering customer-facing digital platforms and enterprise integration solutions.

Responsibilities

• Assess and modernize legacy applications using cloud-native patterns.
• Design scalable Azure PaaS solutions and Gemini Enterprise for CX Lead migration to Azure.
• Transform monolithics into microservices and API-driven architectures.
• Develop modernization roadmaps aligned to business goals and CX objectives.
• Establish architectural standards, governance, security, and operations best practices.
• Architect resilient, scalable Azure/ cloud solutions on Azure and GCP.
• Design cloud platforms using Azure App Services, AKS, Functions, API Management, Cosmos DB, and Data services.
• Implement Infrastructure as Code with Terraform and Bicep.
• Drive cloud adoption focusing on reliability, security, cost optimization, and performance.

Job description

Aimlroles is seeking a seasoned software architect to lead enterprise cloud modernization across Azure and Google Cloud. You will design scalable AI-enabled platforms, modernize monolithic apps into microservices, and govern security and DevSecOps practices.

Responsibilities span architecture, integration, data strategy, and AI governance, with a focus on reliability, performance, and cost optimization in large-scale deployments.