Field Solutions Architect Manager, Applied AI, Google Cloud

Minimum qualifications:

• Bachelor’s degree in Computer Science or equivalent practical experience in Software Engineering, Site Reliability Engineering, or Development and Operations.
• 8 years of experience in cloud computing and technical customer-facing roles, and Python.
• 5 years of experience in a technical consulting, systems architecture, or sales engineering role, including experience presenting technical roadmaps to C-suite executives.
• Experience developing and deploying agentic solutions utilizing tools, multi-agent workflows and scalable RAG systems.
• Experience managing end-to-end technical project lifecycles and resource allocation for enterprise-level global clients.

Preferred qualifications:

• Master’s degree or PhD in AI, Computer Science, or a related technical field.
• 2 years of experience in pre-sales management.
• Experience in architecting AI solutions within infrastructures, ensuring data sovereignty and secure governance.
• Experience in designing intuitive interfaces for complex AI and agentic systems, prioritizing context engineering, transparency, and explainability to foster user trust.
• Ability to design end to end secure, observable multi-agent systems using design patterns (e.g., ReAct, self-reflection,etc), state management, and tool-calling protocols.

About the job

As the Manager of the Applied AI Field Solutions Architect (FSA) team, you will lead a squad of AI/ML engineers across North America who bridge the gap between frontier AI products and production-grade reality within customers. You are responsible for a team that doesn't just consult, but codes, debug and jointly deploys bespoke agentic solutions directly within customer environments.

In this role, you will provide technical mentorship to your team while balancing high-level alignment with Product, Engineering, and Google Cloud Regional Sales leadership. Your mission is to empower and unblock your team as they resolve production-level obstacles, including data readiness issues, integration complexities, and state-management tests that hinder AI from achieving enterprise-grade maturity.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge 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.
The US base salary range for this full-time position is $177,000-$263,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
The US base salary range for this full-time position is $177,000-$263,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Serve as the ultimate technical lead, establishing code standards, architectural best practices, and benchmarks to elevate engineering excellence across the team.
• Partner with Sales and Technical Leadership to define requirements for high-value opportunities, deploying specialized experts (e.g., agentic systems in customer experience) to key accounts.
• Lead technical hiring for Applied AI Field Solutions Architects, evaluating AI Agent expertise, systems engineering, and coding skills to build a engineering squad.
• Identify skill gaps in emerging technologies (e.g., Model Context Protocol (MCP), tool-calling, and foundation models), ensuring the team maintains subject matter expertise in an evolving AI stack.
• Collaborate with Product and Engineering to resolve blockers and translate field insights into roadmaps while building internal