AI Customer Engineer, Digital Natives, Google Cloud

About the job

The Google Cloud Platform team helps customers transform and build what's next for their business - all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers - developers, small and large businesses, educational institutions and government agencies - see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.

As a AI Customer Engineer (CE), you will partner with technical sales teams to differentiate Google Cloud to 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 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.

It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll be 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 business cycle from technical evaluation through customer ramp.
• Combine business 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.
• 6 years of experience with cloud native architecture in a customer-facing 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.

Preferred qualifications:

• Experience in 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 engaging with, or presenting to, technical stakeholders or executive leaders.
• 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.