Drive innovation as a Cloud AI Engineer within the Technical Onboarding Center. This role focuses on enhancing customer adoption of Gemini Enterprise and the Google Cloud Agentic AI suite. You will architect and implement production-ready solutions, leveraging cutting-edge AI technologies to meet customer needs and overcome technical hurdles.

Google Cloud empowers organizations to digitally transform and innovate. We deliver robust, scalable cloud solutions that assist developers and businesses globally in solving critical challenges and achieving growth. Join a dynamic team focused on shaping the future of how businesses leverage technology.

Spearhead the global implementation of Gemini Enterprise solutions, employing Agent Development Kits to address complex, large-scale technical challenges. Act as a primary technical advisor to C-suite executives at key global accounts, influencing AI strategy and accelerating Gemini Enterprise adoption.

Translate intricate architectural obstacles into actionable requirements for Google's engineering teams, driving product roadmap enhancements. Present best practices and high-impact technical strategies to executive boards and stakeholders, securing significant technical achievements. Serve as a thought leader and mentor within Google Cloud, elevating engineering expertise and establishing best practices for agentic AI architectures.

Requires a Bachelor's degree in Computer Science or a related technical field, or equivalent practical experience, coupled with 6 years in software engineering, enterprise cloud architecture, or technical consulting. Additionally, 3 years of experience in advanced AI/ML architecture, deploying generative AI applications, intelligent agents, or LLM-powered solutions globally is essential.

Proficiency in Python, including the implementation and deployment of tiered microservices applications, is necessary. 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 also required. Preferred qualifications include advanced degrees, experience with multimodal AI, AI security, LLM governance, and building resilient RAG systems.