AI Outcome Customer Engineer, Forward Deployed Engineering, Google Cloud
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Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 7 years of experience troubleshooting technical issues for internal/external partners or customers.
- Experience in either system design or reading code (e.g., Java, C++, Python).
- Experience with Interfaces (APIs), Enterprise Content Management (ECMs), identity, cloud infrastructure, or AI/ML model deployments.
Preferred qualifications:
- Experience leading technical delivery strategies, debugging systems, and interfacing directly with core product/engineering organizations.
About the job
As an AI Outcome Customer Engineer (OCE) 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 direct 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, direct technical implementation, and technical project leadership to realize the value of Google Cloud AI with our most strategic customers.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Spain: €120000 - €123000 (EUR) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
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.
- Drive sustainable product usage to help customers realize ongoing business value.
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