Field Solutions Architect, Applied AI

About the Field Solutions Architect, Applied AI Role at Google

As a Field Solutions Architect (FSA) in Applied AI at Google, you will act as the "Agent Engineer" and be the primary delivery arm for our customers' most critical AI initiatives. Your role involves transforming initial conversational prototypes into production-ready solutions, overseeing the entire engineering lifecycle, from conceptualization to delivering real-world business value and scalable, secure AI systems. This is a high-travel, high-impact position focused on leading technical delivery for Conversational AI pilots and establishing the first Customer User Journeys (CUJs) for our largest customers directly at their sites. Success in this role requires a deep understanding of software engineering, MLOps, and cloud infrastructure.

Google Cloud is dedicated to accelerating every organization’s ability to digitally transform its business and industry. We provide enterprise-grade solutions leveraging Google’s cutting-edge technology and tools designed for sustainable development. Trusted by customers in over 200 countries and territories, Google Cloud serves as a key partner in enabling growth and resolving critical business challenges.

Minimum Qualifications

• Bachelor’s degree in Computer Science or equivalent practical experience in Software Engineering, Site Reliability Engineering (SRE), or DevOps.
• 6 years of experience in Python and experience architecting scalable AI systems on cloud platforms.
• Experience deploying conversational agents using code-based frameworks and building in real-time with customers utilizing modern generative AI tools.
• Experience in deploying resources via Terraform or similar tools to automate the setup of agents, functions, and networking.
• Experience building full-stack applications (not just scripts) that interact with enterprise IT infrastructures, and in developing and driving customer projects forward in a timely manner.

Preferred Qualifications

• Master’s 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 patterns like ReAct, self-reflection, and hierarchical delegation.
• Experience debugging Agent logic (ReAct loops, Chain of Thought) and optimizing tool selection, including tracing conversation IDs across microservices to identify and resolve failures in real-time.
• Experience connecting agents to enterprise knowledge bases and optimizing Retrieval-augmented generation (RAG) chunking to prevent hallucinations.
• Ability to troubleshoot live, high-traffic systems during critical windows.
• Ability to travel up to 50% of the time.

Responsibilities

• Lead the development of Conversational AI and CX applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable ROI.
• Architect and code conversational flows optimized for seamless integration between Google’s Conversational AI products and customers’ live infrastructure, including APIs, legacy data silos, and security perimeters.
• Build high-performance evaluation (Eval) pipelines and observability frameworks to optimize agentic workloads, focusing on reasoning loops, tool selection, reducing latency, and maintaining production-grade security and networking.
• Identify repeatable field patterns and technical "friction points" within Google’s AAI stack, converting them into reusable modules or product feature requests for engineering teams.
• Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.

Key skills/competency

• Python
• Cloud AI Systems
• Generative AI
• Conversational AI
• MLOps
• Terraform
• Agentic Workflows
• Retrieval-Augmented Generation (RAG)
• Debugging AI Agents
• Enterprise Infrastructure Integration