Join a pioneering team on Google Cloud, dedicated to empowering customers to build and innovate using cutting-edge cloud technology. As a Cloud AI Engineer, you will be instrumental in guiding strategic clients through the adoption of Gemini Enterprise and the broader Google Cloud Agentic AI suite. This role offers a unique opportunity to shape the future of business technology by understanding client needs and driving the implementation of advanced AI solutions.

You will architect and deploy production-ready applications leveraging Gemini Enterprise and core Google AI products, directly impacting customer workflows. By removing technical obstacles, you'll ensure high adoption rates and successful deployments, utilizing Google's advanced technology for performance monitoring, debugging, and troubleshooting complex agent behaviors. Lead the end-to-end execution of Google Cloud Platform solution adoption for our valued customers.

Spearhead the global implementation and delivery of Gemini Enterprise solutions, employing Agent Development Kits (ADKs) to tackle intricate, enterprise-scale technical challenges.

Serve as the primary technical advisor to C-suite executives at Google's most critical global accounts, influencing their AI strategy and accelerating Gemini Enterprise adoption.

Contribute to the core product roadmap by translating complex architectural hurdles into actionable requirements for Google's engineering teams.

Present best practice recommendations and high-impact technical strategies to executive boards and key stakeholders to secure significant technical achievements.

Act as a thought leader and mentor within the Google Cloud organization, enhancing engineer technical capabilities and establishing best practices for agentic AI architectures.

A Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience is required. Possess 6 years of experience in software engineering, enterprise cloud architecture, or technical consulting.

Demonstrate 3 years of experience in advanced AI/ML architecture, including the deployment of generative AI applications, intelligent agents, or LLM-powered solutions on a global scale.

Proficiency in Python is essential, encompassing the implementation and deployment of tiered microservices applications. Additionally, experience in deploying production-grade cloud infrastructure or architecting and building cloud-based data solutions such as enterprise data warehouses, data lakes, and data pipelines is necessary.