Minimum qualifications:

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages (e.g., Python).
• Experience taking production-grade AI-driven solutions from conception to launch and architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
• Experience building pipelines for structured and unstructured data using both vector databases and retrieval augmented generation like architectures to power enterprise AI solutions.

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, Agent Development Kit (ADK)) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
  
• Experience leading technical discovery sessions.
• Knowledge of large language model native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.

About the job

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.

As a GenAI Forward Deployed Engineer (FDE) at Google Cloud, you will be an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. Unlike traditional advisory roles, you will function as an "innovator-builder," moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment. This role is designed for high-agency engineers with a founder’s mindset. You will address blockers to production including solving the integration complexities, data readiness issues, and state-management challenges that prevent AI from reaching enterprise-grade maturity.

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.