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 Customer Engineer specializing in Cloud Platform, you will partner with technical Sales teams to differentiate Google Cloud by demonstrating how to build, tune, and deploy the systems. You will serve as a technical expert responsible for the full life-cycle of Cloud Engineering, from advanced model evaluation to deployment strategies. You will engage with customers to understand their business and technical requirements, and present solutions on Google Cloud. You will blend sales and market knowledge, and engineering capability to help customers move from notebook experimentation to deployed, inference endpoints.

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.

Responsibilities

• Provide technical consultation to customers on model development, evaluation, and deployment, act as a technical advisor and building customer relationships. Drive the technical win for computing workloads within Cloud Platform to ensure successful adoption, primarily support the business cycle from technical evaluation through customer ramp.

• Combine business strategies with development and prototyping to provide functional, customer-tailored solutions that secure buy-in from customer domain experts.

• Recommend integration strategies, enterprise architectures, platforms, and application infrastructure required to implement a complete solution on Google Cloud.

• Work directly with Google Cloud products to demonstrate and prototype integrations in customer and partner environments. Provide critical feedback from customer engagements to Product and Engineering teams to improve architectures and solutions.

• Leverage learnings from customer engagements to contribute to reusable solutions and assets with the Go-To-Market (GTM) team.

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 deploying algorithmic analysis models to production environments and local development, including containerized inference endpoints.

• Experience with model architectures including encoders, decoders, and transformers.

• Experience with algorithmic analysis model development and deployment, including the frameworks (e.g., Py Torch, JAX, Tensor Flow) and accelerators.

• Experience working with programming to demo, prototype, or workshop with customers.

Preferred qualifications

• Master's degree in Computer Science, Engineering, Mathematics, a technical field, or equivalent practical experience.

• Experience in data and information management and in instrumenting code with Observability tools to monitor agent performance and latency.

• Experience in architecting and developing software or infrastructure for distributed systems and experience with Agentic frameworks such as Lang Graph, Semantic Kernel, or the Google Agent Development Kit (ADK).

• Experience in context engineering, including context caching strategies, multimodal snalysis, JSON structured output generation, and function calling optimization.