Partner Forward Deployed Engineer Iii, Generative AI (french, German)

Google Google · Big Tech · London, United Kingdom

Partner Forward Deployed Engineer III for Generative AI at Google Cloud, focusing on building and deploying complex AI applications and agentic workflows for enterprise clients. The role involves architecting solutions, building evaluation pipelines, and ensuring production-grade quality and performance, with a requirement for French and German language fluency.

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.
  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 in one or more programming languages
  • ML infrastructure (model deployment, model evaluation, optimization, data processing, debugging)
  • GenAI techniques (LLMs, Multi-Modal, Large Vision Models) or GenAI-related concepts
  • French language fluency
  • German language fluency

Nice to have

  • Master’s degree or PhD 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 agent patterns like ReAct, self-reflection, and hierarchical delegation
  • LLM-native metrics (tokens/sec, cost-per-request)
  • Techniques for optimizing state management and granular tracing
  • Implementing secure agentic workflows incorporating MCP, tool-calling, and OAuth-based authentication

What the JD emphasized

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

Other signals

  • Generative AI
  • Agentic workflows
  • Production-grade systems
  • Customer success