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 an Artificial Intelligence (AI) Adoption Customer Engineer (CE) you will embed with our most strategic customers to drive the adoption of Google Cloud enterprise AI products, acting as a trusted advisor to customer stakeholders, partners, and internal product and engineering teams. You will develop, manage and execute the AI deployment plan, by providing technical leadership, applying your skills in Enterprise Architecture, consultative delivery, Applied Artificial Intelligence engineering, and AI solution engineering. You will have a direct impact on the velocity of customer adoption and business outcomes realized through the successful deployment of enterprise AI solutions and use cases. You will blend delivery expertise, market and industry knowledge, technical implementation, and technical project leadership to realize the value of Google Cloud AI with our most strategic customers.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
- Develop and orchestrate a structured, end-to-end deployment plan across customer, professional services, and partner teams, onboarding the implementation team, clearing blockers, managing timelines and progress, and ensuring readiness.
- Employ code development, debugging, or systems design to resolve technical blockers and accelerate customer time-to-value.
- Drive and track progress of the initial and ongoing adoption of enterprise AI, accelerating customers from initial agreement to business outcomes as quickly as possible.
- Identify and develop opportunities for new enterprise AI use cases during project execution.
- Drive sustainable product usage to help customers realize ongoing business value.
Qualifications
Minimum qualifications:
- Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- 10 years of experience with cloud native enterprise architecture in a customer-facing or support role.
- Experience in deployment planning, delivery orchestration, or change management.
- Experience developing AI/Generative AI (GenAI) solutions utilizing AI tools and designing multi-agent workflows and retrieval-augmented generation (RAG) systems.
- Experience engaging with, presenting to, or influencing technical stakeholders or executive leaders.
- Ability to travel up to 30% of the time.
Preferred qualifications:
- Master’s degree or PhD in AI, Computer Science, or a related technical field.
- Experience with development or implementation of AI agents in an enterprise environment.
- Experience simultaneously guiding multiple customers through the organizational change and delivery preparation to adopt new, cloud-powered business processes.
- Experience working with product and support teams and using technical proficiencies to resolve blockers and accelerate customer delivery.
- Background in architecting AI solutions within infrastructures, ensuring data sovereignty and secure governance.
- Ability to perform deep ‘discovery’ interviews to find the true business problem and translate complex hardware/AI constraints for C-suites and deep-technical teams.