AI Customer Engineer, Manufacturing, Semiconductor, Google Cloud (english, Korean)

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 secure, data-connected solutions and acting as a technical authority.

What you'd actually do

  1. 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.
  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, using these engagements to build reusable GTM assets and channel critical customer feedback to Product and Engineering teams.
  4. 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.
  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
  • programming languages
  • debugging
  • systems design
  • prototyping
  • demos
  • customer workshops
  • technical stakeholders
  • executive leaders
  • manufacturing industry
  • semiconductor industry
  • 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 authority
  • enterprise AI integration patterns
  • customer-tailored solutions

Other signals

  • integrating Generative AI into complex enterprise environments
  • architecting secure, and data-connected solutions
  • technical authority on integrating Generative AI