Practice Customer Engineer, Artificial Intelligence, Google Cloud

About the job

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 a Practice Customer Engineer specializing in Gemini enterprise, you will partner with technical Sales teams to differentiate Google Cloud to the customers. You will serve as the technical authority on integrating Generative Artificial Intelligence (AI) into enterprise environments, moving beyond the 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 business expertise, 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 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

• 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 implement a complete solution on Google Cloud.
• Combine business strategies with development and prototyping to provide functional, customer-tailored solutions that secure buy-in from customer domain experts.
• Provide technical consultation to customers on enterprise AI integration patterns, 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 required, acting as a public advocate for Google Cloud.

Qualifications

Minimum qualifications:

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

Preferred qualifications:

• Experience developing agents using frameworks such as langgraph, semantic kernel, or Google AI Agent Development Kit (ADK).
• Experience with integration Platform as a Service (iPaaS), Application Programming Interface (API) Gateways, or Enterprise Service Buses (ESBs) in a cloud environment.
• 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 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.