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 business strategies with direct development and prototyping to provide functional, customer-tailored solutions that secure buy-in from customer domain experts.
Provide technical consultation to customers on enterprise AI integration patterns, 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 required, 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, with application development and integration.
Experience with search systems including retrieval, ranking, and search quality tuning.
Experience with coding in Python, JavaScript or TypeScript, Go, or Java, to demo, prototype, or workshop integration patterns with customers.
Experience in architecting solutions that integrate AI models using agents with enterprise data sources using patterns like (retrieval-augmented generation) RAG, Text-to-SQL, and semantic search.
Experience with presenting to technical stakeholders and executive leaders.
Preferred qualifications
Experience with functional evaluation metrics used to assess model quality and agent quality.
Experience with iPaaS, API gateways, or enterprise service buses (ESBs) in a cloud environment.
Experience developing agents using frameworks such as LangGraph, semantic kernel, or the Google AI agent development kit (ADK).
Knowledge of observability constructs including distributed tracing, logging, and audit logging for AI applications.
Knowledge of application integration governance and security, including open authorization 2.0 (OAuth2) flows and short-lived credential management.
Understanding of integration patterns using OpenAPI and model context protocol (MCP) to connect AI agents with business systems and API gateways.
Bachelor's degree or equivalent practical experience.
6 years of experience with cloud-native architecture in a customer-facing or support role, with application development and integration.
Experience with search systems including retrieval, ranking, and search quality tuning.
Experience with coding in Python, JavaScript or TypeScript, Go, or Java, to demo, prototype, or workshop integration patterns with customers.
Experience in architecting solutions that integrate AI models using agents with enterprise data sources using patterns like (retrieval-augmented generation) RAG, Text-to-SQL, and semantic search.
Experience with presenting to technical stakeholders and executive leaders.
