Field Solutions Architect, Generative AI, Google Cloud
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Minimum qualifications:
- Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- 6 years of experience in providing production-grade AI solutions to external or internal customers, and experience architecting AI systems on cloud platforms.
- Experience leading technical discovery sessions with executive stakeholders and engineering teams to define AI and hardware infrastructure requirements.
Preferred qualifications:
- Master’s degree or PhD in AI, Computer Science, or a related technical field.
- Experience designing data engineering pipelines using BigQuery and Vertex AI for analytics.
- Experience delivering L400-level architecture for production-grade ML systems in enterprise environments.
- Experience in Vertex AI pipelines, Kubeflow, or MLflow to implement CI/CD/CT automation and experimentation.
- Experience in model governance, monitoring, and both batch and online serving strategies.
About the job
In this role, you will be an embedded builder who bridges the gap between Artificial Intelligence (AI) products and production-grade reality within customers. You will manage blocker to production including solving the integration complexities, data readiness issues, and state-management tests that prevent AI from reaching enterprise-grade maturity. You will be providing deployment of AI systems and act as a feedback loop, transforming field insights into Google Cloud’s future product road map.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.Responsibilities
- Serve as the lead developer for 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 the connections between Google’s AI products and customers' live infrastructure, including APIs, legacy data silos, and security perimeters.
- Build evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for safety.
- 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.
- Co-build with customer engineering teams to instill Google-grade development best practices, ensuring project success and end-user adoption.
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