Forward Deployed Engineer III, GCC (French, German)
Summary
Develops production-grade AI/ML agentic systems (e.g., multi-agent workflows, MCP servers) for Google Cloud, bridging AI models with enterprise infrastructure while optimizing performance, safety, and scalability for global customers.
- Lead development of 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.
- Design and code the "connective tissue" between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
- Develop 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.
- Partner with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.
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, 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 and German fluently to support client relationship management in this region.
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 complex patterns like ReAct, self-reflection, and hierarchical delegation.
- Knowledge of Large Language Model"(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.