Field Solutions Architect, Generative AI

About the Role

As a Field Solutions Architect, Generative AI at Google Cloud, you are an embedded builder who bridges the gap between AI products and production-grade reality within customers. You function as a builder-consultant, moving beyond architecture to code, debug, and jointly ship agentic solutions directly within the customer’s environment.

In this role, you will handle 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 accounts, you serve a dual purpose: providing white glove deployment of 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.
• 6 years of experience shipping production-grade AI-driven solutions to external or internal customers.
• Experience architecting scalable AI systems on cloud platforms (e.g., Google Cloud Platform).

Preferred Qualifications

• Master’s degree 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.
• Proven ability to implement secure agentic workflows incorporating MCP, tool-calling, and OAuth-based authentication.

Responsibilities

• Serve as the lead developer for AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, Model Context Protocol (MCP) servers) that drive 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 evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for 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 project success and end-user adoption.

Key Skills/Competency

• Generative AI
• Cloud Architecture
• Multi-agent Systems
• Google Cloud Platform
• Production-grade AI
• API Integration
• Data Readiness
• LLM Optimization
• Agentic Workflows
• Customer Collaboration