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Forward Deployed Engineer for AI Systems

Interview Guide: Google Cloud GenAI FDE

Explore the Google Cloud GenAI Forward Deployed Engineer interview process, including recruiter screening, coding, collaborative vibe-coding, agentic system design, and behavioral assessments. Learn key role requirements and effective strategies to demonstrate technical expertise, customer-facing judgment, and collaborative skills for this enterprise AI deployment role.

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Google Cloud is Google’s enterprise cloud division, home to Vertex AI, BigQuery, and the infrastructure stack that large organizations use to build and run production AI systems. The company launched its Forward Deployed Engineer function for its GenAI track to move enterprise customers from AI pilots into production deployments across the Gemini model family and Vertex AI platform. The role sits within Google Cloud's professional services organization and is available at multiple seniority levels. Before examining the loop, it helps to understand what the role involves and what Google is looking for.

The role and its requirements

The Google Cloud GenAI Forward Deployed Engineer owns the technical delivery of AI systems inside customer environments. The work spans discovery, architecture, build, and handoff across the full deployment lifecycle.

According to Google’s published job descriptions, core responsibilities include the following.

  • Transitioning rapid AI prototypes into production-grade agentic workflows, including multi-agent systems and MCP servers, built inside the customer’s environment.

  • Architecting and coding connections between Google’s AI products and the customer’s live infrastructure, including APIs, legacy data systems, and security perimeters.

  • Building evaluation pipelines and observability frameworks to ensure agentic systems meet accuracy, safety, and latency requirements.

  • Identifying repeatable patterns across customer engagements and feeding those back to Google’s engineering teams as product feature requests.

On the requirements side, Google expects a Master’s degree or PhD in a technical field, a higher academic bar than most roles in this space. Technical requirements include production experience with multi-agent frameworks such as LangGraph, CrewAI, and Google ADKGoogle Agent Development Kit (ADK) is a framework for building and orchestrating agents on Vertex AI., proficiency with RAG pipeline design, knowledge of LLM-native performance metrics such as tokens per second and cost per request, and experience deploying AI systems on cloud platforms with a ...