Field Solutions Architect, GenAI, Google Cloud

About Google

Google is committed to fostering a diverse and inclusive workplace. They are an equal opportunity and affirmative action employer, considering qualified applicants regardless of criminal histories, consistent with legal requirements. Google also provides accommodations for applicants with disabilities or special needs.

About The Job

As a Field Solutions Architect, GenAI at Google Cloud, you will function as an embedded innovator-builder, bridging the gap between cutting-edge AI products and real-world production deployments within customer environments. This role moves beyond traditional advisory, requiring you to code, debug, and jointly ship bespoke agentic solutions directly with customers. You will address crucial blockers to production, including integration complexities, data readiness issues, and state-management challenges, ensuring AI solutions achieve enterprise-grade maturity. By embedding with strategic accounts, you will provide 'white glove' deployment of complex AI systems and act as a vital feedback loop, translating field insights into Google Cloud’s future product roadmap.

This is an exciting opportunity to join Google Cloud’s Go-To-Market team, driving the AI revolution globally. You will leverage Google's brand credibility and its advanced AI portfolio, including frontier Gemini models and the complete Vertex AI platform, to solve significant business problems. The collaborative culture offers direct access to DeepMind's engineering and research experts, empowering you to address customer challenges and define the new cloud era.

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.

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, converting them into reusable modules or formal product feature requests for the 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

• Generative AI
• Solution Architecture
• Cloud AI Platform (GCP)
• Python & Typescript
• Multi-agent Systems
• RAG Architectures
• Vector Databases
• AI System Deployment
• Customer Engagement
• Product Feedback Loop