Cloud AI Engineer, Technical Onboarding Center

About the job:
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.

Responsibilities:
Lead and oversee the global delivery and implementation of Gemini Enterprise solutions, utilizing Agent Development Kits (ADKs) to solve highly complex, enterprise-scale technical challenges.
Serve as the trusted technical advisor to C-suite executives at Google’s most strategic global accounts, shaping their overarching AI strategy and accelerating the adoption of Gemini Enterprise.
Influence the core product roadmap by translating complex architectural challenges into actionable requirements for Google's engineering teams.
Deliver leading practice recommendations and high-stakes technical presentations to executive boards and key business stakeholders to secure massive-scale technical wins.
Act as a thought leader and mentor across the Google Cloud organization, elevating the technical acumen of engineers and shaping best practices for agentic AI architectures.

Minimum qualifications:
Bachelor's degree in Computer Science, or a related technical field, or equivalent practical experience.
6 years of experience in software engineering, enterprise cloud architecture, or technical consulting
3 years of experience in advanced AI/ML architecture, deploying generative AI applications, intelligent agents, or LLM-powered solutions globally.
Experience in Python including implementation and deployment of tiered microservices applications.
Experience deploying production-grade cloud infrastructure or architecting and building cloud-based data solutions (e.g., enterprise data warehouses, data lakes, data pipelines, and associated technologies).

Preferred qualifications:
Bachelor’s degree, Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
Experience pioneering multimodal AI solutions (text, image, video, and voice) to solve novel, previously unsolved enterprise workflow challenges.
Deep understanding and applied experience with AI security, Large Language Model (LLM) governance, enterprise guardrails, and global compliance requirements (e.g., data privacy regulations, data sovereignty).
Excellence in architecting highly resilient, globally distributed Retrieval-Augmented Generation (RAG) systems and securely connecting LLMs with disparate legacy enterprise databases.
Recognized industry thought leadership (e.g., patents, published research, keynotes) in Generative AI, agentic workflows, or large-scale cloud data infrastructure.