Field Solutions Architect IV, GenAI, Google Cloud

Minimum qualifications:

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 10 years of experience building and shipping production-grade AI-driven solutions to external or internal customers using Python, Typescript or comparable languages.
• Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform or Cloud Computing Platform).
• Experience leading technical discovery sessions with business stakeholders and engineering teams to define AI and hardware infrastructure requirements.
• Experience building pipelines for structured and unstructured data, incorporating vector databases and retrieval-augmented generation (RAG) like architectures for AI solutions.

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 complex patterns like ReAct, self-reflection, and hierarchical delegation.
• Knowledge of "large language model-native" metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.

About the job

As a Field Solutions Architect at Google Cloud, you will be an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. Unlike traditional advisory roles, you will function as an "innovator-builder," moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment.

In this role, you will address blockers to production including solving the integration complexities, data readiness issues, and state-management challenges that prevent AI from reaching enterprise-grade maturity. By embedding with strategic accounts, you will serve a dual purpose: providing "white glove" deployment of complex AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.

It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours.
The US base salary range for this full-time position is $227,000-$320,000 + 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 $227,000-$320,000 + 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 a direct developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, model context protocol servers) that drive measurable return on investment.
• Architect and code the "connective tissue" between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters as part of an expert team.
• Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety and latency.
• Identify repeatable field patterns and friction points in Google’s AI stack,