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 and German fluently to support client relationship management in this region.

Preferred qualifications:

• Master’s 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 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 go-to-market (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.

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.
• 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.