As a Customer Engineer specializing in Gemini Enterprise, you will partner with technical Sales teams to differentiate Google Cloud to customers. You will serve as the technical authority on integrating Generative AI into complex 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 persuasively present practical and useful solutions on Google Cloud. You will blend sales prowess, 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.
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 6 years of experience with cloud native architecture in a customer-facing or support role.
- Experience with cloud engineering, on-premise engineering, virtualization, or containerization platforms.
- Experience engaging with, or presenting to, technical stakeholders or executive leaders.
- Experience in programming languages, debugging, systems design, prototyping, demos, or customer workshops.
- Ability to communicate in English and Korean fluently as this is a customer-facing role that requires interactions in English and Korean with local stakeholders.
Preferred qualifications:
- Master's degree in Computer Science, Engineering, Mathematics, a technical field, or equivalent practical experience.
- Experience developing agents using frameworks like LangGraph, Semantic Kernel, or Google AI ADK.
- Experience with integration platform as a service (iPaaS), application programming interface (API) gateways, or enterprise service buses (ESBs) in a cloud environment.
- Knowledge of Application Integration Governance and Security, including OAuth2 and short-lived credentials (SPIFFE/SPIRE).
- Knowledge of Observability constructs including Distributed Tracing, Logging, and Audit Logging for AI.
- Understanding of integration patterns (OpenAPI/Model Context Protocol (MCP) to connect AI agents with business systems and API Gateways, and familiarity with functional evaluation metrics for Model/Agent Quality.