Drive the technical win for workloads within Gemini enterprise to ensure adoption, support the business cycle from technical evaluation through customer ramp. Recommend integration strategies, enterprise architectures, platforms, and application infrastructure required to successfully implement a complete solution on Google Cloud.
Combine sales strategies with development and prototype to provide functional, customer-tailored solutions that secure buy-in from customer domain experts.
Provide technical consultation to customers on enterprise AI integration patterns, and act as a technical advisor and build customer relationships.
Work with Google Cloud products to demonstrate and prototype integrations in customer and partner environments. Work within product and engineering management systems to document, prioritize, and drive resolution of customer feature requests and issues.
Travel to customer sites, conferences, and other related events as needed, and act as a public advocate for Google Cloud.
Minimum qualifications:
Bachelor's degree or equivalent practical experience.
6 years of experience with cloud native architecture in a customer-facing or support role.
Experience in architecting solutions that integrate AI models using agents with enterprise data sources using patterns like RAG, Text-to-SQL, and semantic search.
Experience with coding in Python, JavaScript or TypeScript, Go, or Java, to demo, prototype, or workshop integration patterns with customers.
Experience with search systems including retrieval, ranking, and search quality tuning.
Experience with presenting to technical stakeholders and executive leaders.
Preferred qualifications:
Experience with Integration Platform as a Service (iPaaS), Application Programming Interface (API) gateways, or Enterprise Service Buses (ESBs).
Experience developing agents using frameworks such as LangGraph, Semantic Kernel, or the Google AI Agent Development Kit (ADK).
Experience with functional evaluation metrics used to assess model quality and agent quality.
Knowledge of observability constructs including distributed tracing, logging, and audit logging for AI applications.
Knowledge of application integration governance and security, including OAuth2 flows and short-lived credential management.
Understanding of integration patterns using OpenAPI and Model Context Protocol to connect AI agents with business systems and API gateways.
