Customer Engineer, Ai, Google Cloud (korean, English)

Google Google · Big Tech · Seoul, South Korea

Customer Engineer for Gemini Enterprise, focusing on integrating Generative AI into enterprise environments on Google Cloud. This role involves partnering with technical sales, architecting secure and data-connected solutions, and acting as a technical authority to drive adoption and prove the value of Google Cloud's AI portfolio.

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 or support role
  • cloud engineering
  • on-premise engineering
  • virtualization
  • containerization platforms
  • engaging with, or presenting to, technical stakeholders or 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
  • integration platform as a service (iPaaS)
  • application programming interface (API) gateways
  • enterprise service buses (ESBs) in a cloud environment
  • Application Integration Governance and Security
  • OAuth2
  • short-lived credentials (SPIFFE/SPIRE)
  • Observability constructs including Distributed Tracing, Logging, and Audit Logging for AI
  • integration patterns (OpenAPI/Model Context Protocol (MCP) to connect AI agents with business systems and API Gateways
  • functional evaluation metrics for Model/Agent Quality

What the JD emphasized

  • technical authority
  • architecting secure, and data-connected solutions
  • enterprise AI integration patterns
  • functional, customer-tailored solutions
  • technical stakeholders or executive leaders