Customer Engineer, Cloud AI, Google Cloud

When leading companies choose Google Cloud, it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You listen and deliver what is most helpful for the customer. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.

In this role, you will partner with technical business teams to differentiate Google Cloud to the customers. You will serve as the technical expert on integrating Generative Artificial Intelligence (GenAI) into enterprise environments, moving beyond simple chat interfaces to architecting secure and data connected solutions. You will engage with customers to understand their business and technical requirements, and present solutions on Google Cloud. You will blend market knowledge and technical engagement to prove the value of the Google Cloud AI portfolio.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s 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.

Netherlands: €94000 - €96000 (EUR) + 42.86% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Drive the technical win for workloads within Gemini enterprise to ensure adoption, support the business cycle from technical evaluation through customer ramp. Recommend integration strategies, enterprise architectures, platforms, and application infrastructure required to successfully implement a complete solution on Google Cloud.
• Combine sales strategies with development and prototype to provide functional, customer-tailored solutions that secure buy-in from customer domain experts.
• Provide technical consultation to customers on enterprise AI integration patterns, and act as a technical advisor and build customer relationships.
• Work with Google Cloud products to demonstrate and prototype integrations in customer and partner environments. Work within product and engineering management systems to document, prioritize, and drive resolution of customer feature requests and issues.
• Travel to customer sites, conferences, and other related events as needed, and act as a public advocate for Google Cloud.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 4 years of experience with cloud native architecture in a customer-facing or support role.
• Experience in architecting solutions that integrate AI models using agents with enterprise data sources using patterns like RAG, Text-to-SQL, and semantic search.
• Experience coding in Python, JavaScript or TypeScript, Go, or Java to demo, prototype, or workshop integration patterns with customers.
• Experience with search systems including retrieval, ranking, and search quality tuning.
• Experience presenting to technical stakeholders and executive leaders.

Preferred qualifications:

• Experience with iPaaS, Application Programming Interface (API) gateways, or Enterprise Service Buses (ESBs) in a cloud environment.
• Experience developing agents using frameworks such as LangGraph, Semantic Kernel, or the Google AI Agent Development Kit (ADK).
• Experience with functional evaluation metrics used to assess model quality and agent quality.
• Knowledge of observability constructs including distributed tracing, logging, and audit logging for AI applications.
• Knowledge of application integration governance and security, including OAuth2 flows and short-lived credential management.
• Understanding of integration patterns using OpenAPI and Model Context Protocol (MCP) to connect AI agents with business systems and API gateways.