Cloud Field Solutions Architect, Applied AI

Cloud Field Solutions Architect, Applied AI

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. This role involves taking initial conversational prototypes and transforming them into production-ready solutions, owning the engineering life cycle from the "Art of the Possible" to secure, real-world business value. 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 at their sites. A strong understanding of software engineering, Machine Learning Operations (MLOps), and cloud infrastructure is required.

About Google Cloud

Google Cloud empowers organizations to digitally transform their business and industry. We provide enterprise-grade solutions leveraging Google’s cutting-edge technology and tools that help developers build more sustainably. Customers in over 200 countries and territories trust Google Cloud as their partner for growth and solving critical business challenges.

Minimum qualifications:

• Bachelor’s degree in Computer Science or equivalent practical experience.
• 3 years of experience in Python and architecting AI systems on cloud platforms.
• 3 years of experience developing full-stack applications integrated with enterprise IT infrastructures (e.g., ERP, CRM, or legacy databases) and managing technical projects.
• 2 years of experience developing conversational agents using code-based frameworks (e.g., LangChain, Dialogflow CX, or Rasa) and deploying generative AI tools.
• Experience in deploying resources via Terraform or similar tools to automate the setup of agents, functions, and networking.

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 patterns like ReAct, self-reflection, and hierarchical delegation.
• Experience debugging Agent logic and enhancing tool selection, including tracing conversation IDs across micro-services to identify and resolve failures.
• Experience connecting agents to enterprise knowledge bases and enhancing Retrieval-Augmented Generation (RAG) chunking.
• Experience in troubleshooting live, high-traffic systems during critical operations.
• Ability to travel up to 50% of the time as needed.

Responsibilities:

• Serve as the lead developer for Conversational AI applications, transitioning from prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive return on investment.
• Architect and code conversational flows that are not just functional, but enhanced for the "connective tissue" between Google’s Conversational AI products and customers’ live infrastructure, including APIs, legacy data silos, and security perimeters.
• Build evaluation pipelines and observability frameworks to enhance 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 Applied Artificial Intelligence (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 project success and end-user adoption.

Key skills/competency:

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