Unknown Company
<h3>Minimum qualifications:</h3><ul> <li>Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.<br></li> <li>5 years of experience with software development using Python or similar coding languages.</li> <li>Experience architecting AI systems on cloud platforms (e.g., GCP).<br></li> <li>Experience taking production-grade AI-driven solutions from conception to launch for customers.</li> <li>Experience leading technical discovery sessions with customers.</li> <li>Experience building pipelines for structured and unstructured data using both vector databases and retrieval-augmented generation (RAG)-like architectures to power enterprise AI solutions.</li> </ul><h3>Preferred qualifications:</h3><ul> <li>Master’s or PhD in AI, Computer Science, or a related technical field.<br></li> <li>Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and patterns (e.g., ReAct, self-reflection, hierarchical delegation).</li> <li>Knowledge of LLM-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.</li> </ul><h3>About the job</h3><p>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.</p><h3>Responsibilities</h3><ul> <li>Serve as a 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.</li> <li>Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters as part of an expert team.</li> <li>Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.<br></li> <li>Identify repeatable field patterns and friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.<br></li> <li>Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.</li> </ul>