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# Google Cloud Platform AI Architect/FDE

- **Company:** Fusion
- **Location:** Orlando, FL, United States
- **Contract:** Contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Cloud Engineering, Continuous Integration, Electronic Data Interchange (EDI), Software Deployment, Systems Integration, Google Cloud, Prompt Engineering, Spring-boot, Generative AI, Backend, AI-powered Productivity Tools, Information Technology, Enterprise Integration, Machine Learning Operations, Claude, Restful APIs, Microservices
- **Published:** October 16, 2026
- **Apply:** https://www.dice.com/job-detail/71a8fccb-9fce-475d-a158-81c13c11d7a0

## About the Role

12+ years of overall IT experience, with significant experience in cloud architecture, enterprise architecture, integration, or AI/ML solution architecture.

Strong hands-on experience with Google Cloud Platform services and cloud-native architecture patterns.

Deep understanding of Vertex AI, Generative AI solution design, prompt engineering, model integration, AI governance, and production deployment considerations.

Strong Java and Spring Boot background, including microservices architecture, REST APIs, security patterns, and scalable backend design.

Practical experience with enterprise integration, API-led connectivity, data exchange patterns, and integration with legacy and modern platforms.

Ability to evaluate and apply AI productivity tools such as Claude and Antigravity to improve engineering throughput, documentation, solution design, and delivery quality.

Experience working with senior business and technology stakeholders in complex enterprise environments.

Excellent communication, facilitation, consulting, and stakeholder-management skills.

Preferred Qualifications:

Google Professional Cloud Architect or relevant Google Cloud certification.

Experience in healthcare, provider systems, clinical operations, payer/provider integration, or regulated enterprise environments.

Experience with AI governance, security, privacy, responsible AI practices, and data-protection requirements.

Exposure to MLOps, LLMOps, CI/CD, observability, and production support models for AI-enabled platforms.