Forward Deployed Engineer, GenAI, Google Cloud

As an AI Forward Deployed Engineer (FDE) at Google Cloud, you are an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. Unlike traditional advisory roles, you function as a "builder-consultant," moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment. You will manage blocker to production including solving the integration complexities, data readiness issues, and state-management challenges that prevent AI from reaching enterprise-grade maturity. By embedding with strategic accounts, you serve a dual purpose: providing "white glove" deployment of complex AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Minimum qualifications:

• Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
  
• 3 years of experience in Python and relevant machine learning packages (e.g., keras, HF transformers).
• Experience in applied AI, with a focus on building systems around pretrained models (e.g., prompt engineering, fine-tuning, Retrieval-Augmented Generation (RAG), orchestrating model interactions with external tools to deliver solutions).
• Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, etc.) and patterns like ReAct, self-reflection, and hierarchical delegation.

Preferred qualifications:

• Master's degree in Computer Science, Engineering, or a related technical field.
• Experience training and fine tuning models in large scale environments (e.g., image, language, recommendation) with accelerators.
• Experience in systems design with the ability to architect and explain data pipelines, ML pipelines, and ML training and serving approaches.
• Experience working with customers in a technical capacity.
• Knowledge of "LLM-native" metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
• Ability to be action-oriented, with a focus on solving customer problems.