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Forward Deployed Engineer III, Generative AI (French, German)

Google
1 day ago
Full-time
On-site
Zürich, ZH

JobsCloseBy Editorial Insights

Google seeks a Forward Deployed Engineer III, Generative AI (French, German) to join onsite in Zürich. The role requires a foundation in software development, ML infrastructure, and GenAI concepts, with fluency in French or German for client relationships. Preferred candidates hold advanced degrees and experience with multi-agent systems, ADK or LangGraph and CrewAI, and knowledge of LLM-native metrics plus secure agentic workflows. You’ll lead AI applications, connect Google AI products to customer ecosystems, and build evaluation pipelines to drive ROI. To apply, tailor your resume to show deployments, customer collaboration, language proficiency, outcomes, and security or tool-calling capabilities; demonstrate enterprise impact and readiness to work onsite in Zürich.

Minimum qualifications:

  • Bachelor’s degree or equivalent practical experience.
  • 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
  • 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
  • Experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).
  • Ability to communicate in French or German fluently to support client relationship management in this region.

Preferred qualifications:

  • Master's or PhD degree 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 complex patterns like ReAct, self-reflection, and hierarchical delegation.
  • Knowledge of "LLM-native" metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
  • Ability to implement secure agentic workflows incorporating MCP, tool-calling, and OAuth-based authentication.

About the job

Google Cloud’s mission is to make every business successful through AI by combining cutting-edge technology, infrastructure, and talent. AI/ML software engineers in Cloud bridge the gap between pioneering models and a massive product vehicle reaching billions. Our talent density and AI-powered tools drive rapid development, rooted in a culture of empowerment and a bias to action. In this role, you aren’t just building technology; you’re shaping the frontier of enterprise and driving the evolution of advanced models.

Leading with the future, the AI GTM team are trusted AI advisors, scaling across Google to deliver transformative customer experiences that shape the next generation of their business. Partnering to provide deep AI expertise, thought leadership, and industry best practices, we revolutionize customer outcomes.

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

  • 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).
  • Architect and code the "connective tissue" between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
  • Build high-performance evaluation (Eval) pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
  • 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.
  • Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.