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 Customer Engineer (CE) with a specialty in AI, you will partner with technical sales teams to differentiate Google Cloud to our customers. You will serve as a technical expert responsible for accelerating technical wins and adoption of complex, specialized workloads. You will leverage your deep expertise in our most strategic product areas in partnership with Platform CEs, writing code, developing prototypes, proofs-of-concept, and demos to promote new specialized solutions to customers. You will solve AI-centered customer issues and provide a critical feedback loop to influence product development. You will have excellent organizational, communication, and presentation skills, engaging with customers to understand their business and technical requirements, and present solutions on Google Cloud. You will blend business skills, market knowledge, and technical engagement to prove the value of the Google Cloud portfolio.
It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours.
Responsibilities
- Drive the technical solution for complex workloads within AI product areas to ensure rapid and successful adoption, primarily supporting the sales 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.
- Provide deep technical consultation to customers, acting as a technical advisor and building lasting customer relationships. 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.
- Travel to customer sites, conferences, and other related events as required, acting as a public advocate for Google Cloud.
Qualifications
Minimum qualifications:
- Bachelor's degree in a technical field or equivalent practical experience.
- 10 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 Retrieval-Augmented Generation (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.
Preferred qualifications:
- Experience in developing agents using frameworks such as LangGraph, Semantic Kernel, or the Google AI Agent Development Kit (ADK).
- Experience engaging with, or presenting to, technical stakeholders or executive leaders.
- Experience with cloud technologies including SaaS applications, iPaaS, business automation solutions, Cloud infrastructure, Agentic AI, and cloud networking.
- Knowledge of integration patterns using OpenAPI and Model Context Protocol (MCP) to connect AI agents with business systems and Application Programming Interface (API) Gateways.
- Knowledge of observability constructs including distributed tracing, logging, and audit logging for AI applications.