Field Solutions Architect, GenAI, Google Cloud

The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud.

• Serve as the lead developer for AI applications, transitioning from prototypes to production-grade agentic workflows (e.g., multi-agent systems, Master Control Program (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 Application Programming Interfaces (APIs), legacy data silos, and security perimeters.
• Build evaluation pipelines and observability frameworks to ensure agentic systems meet 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 project success and end-user adoption.

• Bachelor’s degree in a Science, Technology, Engineering, and Mathematics or a related field, or equivalent practical experience.
• 6 years of experience in providing production-grade AI solutions to external or internal customers with L400-level in Python, and architecting AI systems on cloud platforms.
• Experience in developing Generative AI (GenAI) solutions with foundation models, first-party model tuning, and advanced Retrieval-augmented generation (RAG) architectures.
• Preferred qualifications: Master’s degree or PhD in Artificial Intelligence, Computer Science, or a related technical field.
• Experience in implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google’s Agent Development Kit (ADK)) and patterns like ReAct, self-reflection, and hierarchical delegation.
• Knowledge of Large Language Model (LLM)-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
• Ability to implement agentic workflows incorporating MCP, tool-calling, and OAuth-based authentication.
• Ability to build full-stack applications that interact with enterprise IT infrastructures, and perform interviews to find the business problem and translate hardware/AI constraints for technical teams.

• 6 years of experience in providing production-grade AI solutions to external or internal customers with L400-level in Python, and architecting AI systems on cloud platforms.
• Experience in developing Generative AI (GenAI) solutions with foundation models, first-party model tuning, and advanced Retrieval-augmented generation (RAG) architectures.
• Preferred qualifications: Master’s degree or PhD in Artificial Intelligence, Computer Science, or a related technical field.
• Experience in implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google’s Agent Development Kit (ADK)) and patterns like ReAct, self-reflection, and hierarchical delegation.
• Knowledge of Large Language Model (LLM)-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
• Ability to implement agentic workflows incorporating MCP, tool-calling, and OAuth-based authentication.
• Ability to build full-stack applications that interact with enterprise IT infrastructures, and perform interviews to find the business problem and translate hardware/AI constraints for technical teams.

Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.