Field Solutions Architect, Applied AI

Field Solutions Architect, Applied AI at Google

The Field Solutions Architect (FSA) in Applied AI at Google is a pivotal role, serving as the "Agent Engineer" and primary delivery arm for Google Cloud customers' most critical AI initiatives. This position focuses on transforming initial conversational prototypes into production-ready solutions, overseeing the entire engineering lifecycle from conceptualization to scalable, secure AI systems that deliver real-world business value.

This is a high-travel, high-impact role focused on leading technical delivery for Conversational AI pilots and establishing the first Customer User Journeys (CUJs) for Google's largest customers at their sites. Your role requires an understanding of software engineering, MLOps, and cloud infrastructure.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s 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 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 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 that are not just functional, but optimized for the "connective tissue" 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, and reducing latency while maintaining production-grade security and networking.
• Identify repeatable field patterns and technical "friction points" in 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

• Applied AI
• Conversational AI
• Agent Engineering
• Python
• Cloud Architecture
• Generative AI
• MLOps
• Full-stack Development
• Terraform
• Customer Solutions