Forward Deployed Engineer Iii, Google Cloud Gtm (french, German)

Google Google · Big Tech · Zürich, Switzerland

Forward Deployed Engineer III for Google Cloud's AI Go-To-Market team, focusing on building and deploying complex AI applications, including agentic workflows and multi-agent systems. The role involves architecting the integration of Google's AI products with customer infrastructure, developing evaluation pipelines and observability frameworks, and identifying improvements for Google's AI stack. Requires experience with ML infrastructure, GenAI techniques, and fluency in French or German.

What you'd actually do

  1. Serve as the lead developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, model context protocol (MCP) servers) that drive measurable Return on investment (ROI).
  2. Architect and code the "connective tissue" between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
  3. Build high-performance evaluation (Eval) pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
  4. Identify repeatable field patterns and technical "friction points" in Google’s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.
  5. Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.

Skills

Required

  • software development
  • ML infrastructure
  • GenAI techniques
  • LLMs
  • Multi-Modal
  • Large Vision Models
  • language modeling
  • computer vision
  • French or German fluency

Nice to have

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

What the JD emphasized

  • production-grade agentic workflows
  • multi-agent systems
  • evaluation (Eval) pipelines
  • observability frameworks
  • agentic systems
  • multi-agent systems
  • agentic workflows

Other signals

  • AI GTM team
  • AI expertise
  • AI portfolio
  • Vertex AI platform
  • DeepMind's engineering and research minds
  • AI revolution
  • AI GTM team