From Google's careers page
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As a Practice AI Customer Engineer (CE), you will partner with technical sales teams to differentiate Google Cloud AI to our customers.
When leading companies choose Google Cloud, it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You listen and deliver what is most helpful for the customer. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.
As a Practice AI Customer Engineer (CE), you will partner with technical sales teams to differentiate Google Cloud AI to our customers.
Taking ownership of the technical engagement, you will lead AI solution design, expertly architecting and sizing environments to meet precise performance, scale, and cost requirements.
You will leverage your deep product expertise in partnership with Platform CEs by writing code and delivering tailored demos and PoC development to tangibly prove the value of new specialized solutions.
You will serve as a technical expert responsible for accelerating technical wins and adoption of complex, specialized workloads. In this role, you will create and lead the AI technical strategy, engaging with customers to understand their broader business goals.
You will drive AI workload qualifications to identify the most impactful, feasible use cases for our clients.
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.
Drive the technical solution for complex workloads within AI product areas to ensure rapid and successful adoption, primarily supporting the business-cycle from technical evaluation through customer ramp.
Combine sales strategies and direct development and prototyping to provide functional, customer-tailored solutions that secure buy-in from customer domain experts.
Act as a trusted technical advisor and Google Cloud advocate by delivering deep technical consultation to build lasting relationships and traveling to customer sites, conferences, and industry events as required.
Leverage learnings from customer engagements to contribute to reusable solutions and assets with the Go-To-Market team.
Work within Product and Engineering management systems to document, prioritize and drive resolution of customer feature requests and issues.
Bachelor's degree or equivalent practical experience.
10 years of experience with cloud native architecture in a customer-facing or support role.
Experience 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 RAG, Text-to-SQL, and semantic search.
Experience with search systems including retrieval, ranking, and search quality tuning.
Experience presenting to technical stakeholders and executive leaders.
Experience developing agents using frameworks such as LangGraph, Semantic Kernel, or the Google AI Agent Development Kit (ADK).
Experience with cloud technologies including SaaS applications, iPaaS, business automation solutions, Cloud infrastructure, Agentic AI, and cloud networking.
Experience working with large enterprise clients, preferably within the Consumer Products and Retail sector.
Knowledge of observability constructs including distributed tracing, logging, and audit logging for AI applications.
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.
10 years of experience with cloud native architecture in a customer-facing or support role.
Experience 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 RAG, Text-to-SQL, and semantic search.
Experience with search systems including retrieval, ranking, and search quality tuning.
Experience presenting to technical stakeholders and executive leaders.
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