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Salary
≈ $51k – $128k per year (Estimated)
Location
In office (Singapore)
Seniority
Senior · 8+ years exp

Confirmed on the employer's own hiring board on Oct 8, 2026. First seen by Alion on Sep 30, 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 Generative Artificial Intelligence (GenAI) Forward Deployed Engineer at Google Cloud, you will be an embedded builder who bridges the gap between frontier Artificial Intelligence (AI) products and production-grade reality within customer environments. You will function as a builder-consultant, moving beyond architecture to code, debug, and jointly ship bespoke agentic solutions. You will solve the integration complexities, data readiness, and state-management issues that prevent AI from reaching enterprise-grade maturity. You will serve a dual purpose: providing "white glove" deployment and acting as a critical feedback loop for Google Cloud’s future product roadmap.It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility-a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era-the market is yours.

Responsibilities

  • Lead the discovery-to-deployment journey, serving as the lead developer for AI applications and transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems and Model Context Protocol servers) that drive measurable return on investment.
  • Bridge the enterprise gap by architecting and coding the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
  • Engineer for production excellence, building high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet the requirements for accuracy, safety, and latency.
  • Act as a product catalyst, identifying repeatable field patterns and technical friction points in Google’s AI stack and converting them into reusable modules or product feature requests for engineering teams.
  • Drive regional leadership by mentoring talent, co-building with customer teams, and influencing cross-functional strategies to up-level organizational technical capabilities.

Qualifications

Minimum qualifications:

  • Bachelor’s degree in Science, Technology, Engineering, Mathematics, a related technical field, or equivalent practical experience.
  • 8 years of experience shipping production-grade AI solutions to external or internal customers.
  • Experience building full-stack solutions that interface with enterprise systems.
  • Experience in Python.
  • Experience architecting AI systems on cloud platforms.

Preferred qualifications:

  • Master’s degree or PhD in AI, Computer Science, or a related technical field.
  • Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google’s Agent Development Kit (ADK)) and patterns like ReAct, self-reflection, and hierarchical delegation.
  • Knowledge of large language model-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
  • Ability to implement secure agentic workflows incorporating Model Context Protocol (MCP), tool-calling, and OAuth-based authentication.
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