Cloud AI Engineer, Technical Onboarding Center

The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.

As a Cloud AI Engineer, you will play a key leadership role in ensuring that strategic customers have the best experience adopting Gemini Enterprise alongside the broader Google Cloud Agentic AI suite of products. You will design and implement robust, production-ready application architectures to support customer use cases, leveraging Gemini Enterprise and core Google AI products. You will work with customers to identify opportunities to transform their workflows with autonomous agents, actively removing technical barriers to ensure successful deployment and high adoption rates of Gemini Enterprise. You will have access to Google’s technology to monitor application performance, debug and troubleshoot complex agent behaviors, and address customer and partner needs. In this role, you will lead the end-to-end and timely execution of adopting Google Cloud Platform solutions to the customer.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities:

• Lead the delivery, deployment, and implementation of Gemini Enterprise solutions, utilizing Agent Development Kits (ADKs) to solve complex, enterprise-scale technical customer challenges.
• Act as a trusted technical advisor to Google’s most strategic customers to shape their AI strategy, drive, and accelerate the adoption of Gemini Enterprise.
• Provide architectural guidance on existing product challenges, collaborate closely with the engineering team to address gaps, and architect scalable workarounds for edge cases.
• Deliver leading practice recommendations and technical presentations adapted to different levels of key business and technical stakeholders (including C-suite executives) to proactively foster Gemini Enterprise adoption and enablement.

Minimum qualifications:

• Bachelor's degree in Computer Science or a related technical field, or equivalent practical experience.
• 3 years of experience in software engineering, cloud architecture, or technical consulting, infrastructure with 2 years of experience in building and deploying GenAI applications, intelligent agents, or LLM-powered solutions to production.
• Experience building and orchestrating agents using ADKs, LangChain, LlamaIndex, AutoGen and in designing modern application architectures, including API design, microservices, and integrating AI models into existing enterprise software systems.
• Experience with Python.

Preferred qualifications:

• Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• Google Cloud Professional certifications.
• Experience implementing scalable RAG architectures and securely connecting LLMs with external productivity tools (e.g., Google Workspace) and complex enterprise databases in high-traffic environments.
• Experience leading technical project deployments, working with engineering teams, or acting as the technical lead on large-scale cloud transformations.
• Experience deploying, configuring, and managing enterprise-grade generative AI platforms (particularly Gemini Enterprise), with an understanding of AI security, Large Language Models (LLM) governance, guardrails, and enterprise compliance requirements (e.g., data privacy regulations).