Ecosystem Engineering Lead, AI and AI Platforms, Google Cloud

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 Ecosystem Engineering lead, you'll join and lead a dedicated team focused on AI and Agentic AI Platforms. You'll serve as a Generative AI subject matter expert, collaborating with Partners, Product Development, and Technical Sales to deliver Google Cloud AI products.

Your role involves helping prospective customers and partners grasp the power of Google AI. You'll explain technical features, assist in designing architectures, build AI-powered applications, and troubleshoot challenges. You'll also guide partners in leveraging Google Cloud’s Generative AI services, including Large Language Models, Agent Platforms, Tooling and specialized Machine Learning (ML) hardware developed by Google, called Tensor Processing Unit.

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.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $233000 - $324000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Serve as a trusted technical advisor to partner executive leadership, including CTOs and Chief Product Officers, aligning multiyear AI roadmaps and exploring advanced technical possibilities.
  
• Lead AI and agentic activities with strategic partners, including executive briefings, ideation, identification of priority use cases, conversion, and comprehensive value realization with Google AI and Agentic platforms.
  
• Identify and qualify business opportunities with partners, comprehend key technical objections, and lead multidisciplinary teams to deliver proof of concepts, custom solution demos, and Generative AI joint innovation.
  
• Partner with product management, engineering, solution management, and Customer Engineering teams to enable partner success, helping developers, creators, and enterprises build products using Google Generative Language APIs.
  
• Recommend integration strategies, enterprise architectures, platforms, and application infrastructure required to successfully implement and deploy complete, robust solutions using best practices on Google Cloud.

Minimum qualifications:

• Bachelor’s degree in Computer Science, Technology, Engineering, Mathematics, or equivalent practical experience.
• 10 years of experience serving in the capacity of a technical Solution Architect in a cloud computing environment in customer/partner facing roles.
• 5 years of experience with Large Language Models (LLMs), Agentic frameworks, Agentic workflows, Open Source, Big Data, Machine Learning, and numerical programming frameworks.
  
• 5 years of experience with AI products, technologies, and infrastructure.
• Experience in a role bridging technical implementation with business strategy (e.g., Partner Architect, Technical PM, or Principal Consultant).

Preferred qualifications:

• Master's degree in Computer Science or a related technical field.
  Experience architecting and developing software or infrastructure for scalable, distributed systems with ML technologies.
  
• Experience building AI and ML solutions, operation frameworks like Kubeflow, and leveraging specific ML architectures.
  
• Understanding of AI Models, Large Language Models, AI agents, AI Agentic workflows, AI specialized infrastructure, and responsible AI practices.
  
• Ability to lead cross-functional teams and deliver end to end solutions to customers/partners.
  
• Ability to quickly learn, understand, and work with new emerging technologies, methodologies and solutions, including leading a team of Ecosystem Engineers.