AI Customer Engineer, Google Cloud (korean, English)

Google Google · Big Tech · Seoul, South Korea

Customer Engineer specializing in Gemini Enterprise, partnering with technical Sales teams to integrate Generative AI into complex enterprise environments. Focuses on architecting robust, secure, and data-connected solutions, blending sales, market knowledge, and technical engagement to prove the value of Google Cloud's AI portfolio.

What you'd actually do

  1. Drive the technical win for complex Gemini Enterprise workloads to ensure rapid adoption, supporting the sales cycle from evaluation through customer ramp. Recommend integration strategies, enterprise architectures, platforms, and application infrastructure for Google Cloud solutions.
  2. Combine sales strategies with direct development and prototyping to provide functional, customer-tailored solutions that secure buy-in from domain experts.
  3. Provide deep technical consultation on enterprise AI integration patterns, acting as a trusted advisor. Leverage engagements to contribute to reusable GTM assets and provide critical customer feedback to Product and Engineering teams.
  4. Work directly with Google Cloud products to demonstrate and prototype integrations. Use Product and Engineering management systems to document, prioritize, and drive resolution of feature requests and issues.
  5. Travel to customer sites, conferences, and events as required, acting as a public advocate for Google Cloud.

Skills

Required

  • cloud native architecture
  • customer-facing role
  • cloud engineering
  • on-premise engineering
  • virtualization
  • containerization platforms
  • technical stakeholders
  • executive leaders
  • programming languages
  • debugging
  • systems design
  • prototyping
  • demos
  • customer workshops
  • English
  • Korean

Nice to have

  • developing agents using frameworks like LangGraph, Semantic Kernel, or Google AI ADK
  • Observability constructs including Distributed Tracing, Logging, and Audit Logging for AI
  • integration patterns using OpenAPI and Model Context Protocol (MCP)
  • functional evaluation metrics used to assess model quality and agent quality

What the JD emphasized

  • technical win
  • customer-tailored solutions
  • enterprise AI integration patterns
  • technical stakeholders
  • customer workshops

Other signals

  • customer-facing
  • technical sales
  • Generative AI integration
  • enterprise environments
  • architecting solutions