Field Solutions Architect, Applied AI

About the Job

As a Field Solutions Architect (FSA) in Applied AI at Google, you will serve as the "Agent Engineer" and the primary delivery arm for our customers' most critical AI initiatives. Your core responsibility is to transform initial conversational prototypes into production-ready solutions, overseeing the entire engineering lifecycle. This includes transitioning from the "Art of the Possible" to delivering real-world business value and implementing scalable, secure AI systems.

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

Google Cloud empowers organizations to digitally transform their businesses across various industries. We provide enterprise-grade solutions leveraging Google’s advanced technology and tools designed for sustainable development. Customers in over 200 countries and territories trust Google Cloud as their partner for growth and resolving critical business challenges.

Responsibilities

• Lead the development of Conversational AI and Customer Experience (CX) applications, evolving rapid prototypes into production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that deliver measurable Return on Investment.
• Architect and code conversational flows, optimizing 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, with a focus 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.
• Collaborate closely with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.

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.

Key skills/competency

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