**Role Overview:**
As a Field Solutions Architect at Google Cloud, you will play a crucial role in bridging the gap between frontier Artificial Intelligence (AI) products and production-grade reality within customers. Acting as a 'builder-consultant', you will not only provide high-level architecture but also code, debug, and deliver tailor-made agentic solutions directly within the customer's environment. Your responsibilities will include addressing blockers to production, solving integration complexities, ensuring data readiness, and managing state issues to elevate Artificial Intelligence (AI) systems to enterprise-grade maturity. Additionally, you will offer 'white glove' deployment of AI systems and provide valuable field insights to shape Google Cloud's future product roadmap. **Key Responsibilities:**
Serve as the lead developer for Artificial Intelligence (AI) applications, transitioning from rapid prototypes to production-grade agentic workflows such as multi-agent systems and Model Context Protocol (MCP) servers that drive measurable Return on Investment (ROI). - Architect and code the 'connective tissue' between Google's AI products and customer's live infrastructure, including Application Programming Interfaces (APIs), legacy data silos, and security perimeters. - Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, and latency. - Identify repeatable field patterns and technical 'friction points' in Google's AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams. **Qualifications Required:**
Bachelor's degree in Science, Technology, Engineering, Mathematics, a related technical field, or equivalent practical experience. - 8 years of experience in providing production-grade AI solutions to external or internal customers with expertise in Python and architecting AI systems on cloud platforms. - Experience leading technical discovery sessions with executive stakeholders (C-suite) and engineering teams to define AI and hardware infrastructure requirements. - Experience building full-stack solutions that interface with enterprise systems. Google Cloud Platform team is dedicated to helping customers transform and build using cloud technology for security, reliability, and scalability. With a focus on understanding customer needs, you will have the opportunity to shape the future of businesses across various sectors. Google Cloud is committed to providing enterprise-grade solutions that leverage cutting-edge technology and tools for sustainable development. **Role Overview:**
As a Field Solutions Architect at Google Cloud, you will play a crucial role in bridging the gap between frontier Artificial Intelligence (AI) products and production-grade reality within customers. Acting as a 'builder-consultant', you will not only provide high-level architecture but also code, debug, and deliver tailor-made agentic solutions directly within the customer's environment. Your responsibilities will include addressing blockers to production, solving integration complexities, ensuring data readiness, and managing state issues to elevate Artificial Intelligence (AI) systems to enterprise-grade maturity. Additionally, you will offer 'white glove' deployment of AI systems and provide valuable field insights to shape Google Cloud's future product roadmap. **Key Responsibilities:**
Serve as the lead developer for Artificial Intelligence (AI) applications, transitioning from rapid prototypes to production-grade agentic workflows such as multi-agent systems and Model Context Protocol (MCP) servers that drive measurable Return on Investment (ROI). - Architect and code the 'connective tissue' between Google's AI products and customer's live infrastructure, including Application Programming Interfaces (APIs), legacy data silos, and security perimeters. - Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, and latency. - Identify repeatable field patterns and technical 'friction points' in Google's AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams. **Qualifications Required:**
Bachelor's degree in Science, Technology, Engineering, Mathematics, a related technical field, or equivalent practical experience. - 8 years of experience in providing production-grade AI solutions to external or internal customers with expertise in Python and architecting AI systems on cloud platforms. - Experienc
