Technical Architect

Job DescriptionJob DescriptionAs a GenAI 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 an "innovator builder," moving beyond high level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customera s environment. This role is designed for high agency engineers with a foundera 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. 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 Clouda s future product roadmap. Job responsibilities a Serve as a developer for complex AI applications, transitioning from rapid prototypes to production grade agentic workflows (e.g., multi agent systems, MCP servers) that drive measurable ROI. a Architect and code the "connective tissue" between Googlea s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters as part of an expert team. a Build high performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency. a Identify repeatable field patterns and friction points in Googlea s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams. a Co build with customer engineering teams to instill Google grade development best practices, ensuring long term project success and high end user adoption. Minimum qualifications a Bachelora s degree in Engineering, Computer Science, a related field, or equivalent practical experience. a 5 years of experience with software development using Python or similar coding languages. a Experience architecting AI systems on cloud platforms (e.g., GCP). a Experience building pipelines for structured and unstructured data using both vector databases and RAG like architectures to power enterprise AI solutions. a Experience taking production grade AI driven solutions from conception to launch for customers. a Experience leading technical discovery sessions with customers. Preferred qualifications a Mastera s or PhD in AI, Computer Science, or a related technical field. a Experience implementing multi agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and complex patterns (e.g., ReAct, self reflection, hierarchical delegation). a Knowledge of "LLM native" metrics (e.g., tokens/sec, cost per request) and techniques for optimizing state management and granular tracing.