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Salary
≈ $88k – $231k per year (Estimated)
Location
In office (Seoul)
Seniority
Senior · 6+ years exp

Confirmed on the employer's own hiring board on Oct 26, 2026. First seen by Alion on Oct 16, 2026. Google scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Google is an American technology company founded in 1998 by Larry Page and Sergey Brin and now the principal subsidiary of Alphabet, headquartered in Mountain View, California. It operates the world's dominant search engine and the advertising system built around it, along with YouTube, Android, Chrome, Gmail, Maps, Workspace and Google Cloud, reaching billions of users across nearly every internet-connected market. The company designs its own silicon in the Tensor Processing Unit line, develops the Gemini foundation models through Google DeepMind, and derives most of its revenue from advertising while cloud has become its fastest growing segment.

About the job

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 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 technical wins for complex Gemini Enterprise workloads to ensure rapid adoption, supporting the sales cycle from evaluation through customer ramp, while recommending integration strategies, enterprise architectures, platforms, and application infrastructure for Google Cloud solutions.
  • Combine sales strategies with direct development and prototyping to provide functional, customer-tailored solutions that secure buy-in from domain experts.
  • Provide deep technical consultation on enterprise AI integration patterns, acting as a trusted advisor, using these engagements to build reusable GTM assets and channel critical customer feedback to Product and Engineering teams.
  • Build and demonstrate integrations directly with Google Cloud products, leveraging Product and Engineering management systems to document, prioritize, and drive resolution of feature requests and issues.
  • Travel to customer sites, conferences, and events as required, acting as a public advocate for Google Cloud.

Qualifications

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.
  • Knowledge of Observability constructs including Distributed Tracing, Logging, and Audit Logging for AI.
  • Deep understanding of integration patterns using OpenAPI and Model Context Protocol (MCP) to connect AI agents with business systems and API gateways.
  • Familiarity with functional evaluation metrics used to assess model quality and agent quality.
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