Customer Engineer, Cloud AI Platform, Google Cloud

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

• Drive the technical win for complex AI/ML workloads within Cloud AI Platform to ensure rapid and successful adoption, primarily supporting the business cycle from technical evaluation through customer ramp.
• Combine sales strategies with direct development and prototyping to provide functional, customer-tailored solutions that secure buy-in from customer domain experts.
• Provide deep technical consultation to customers on model development, evaluation, and deployment, act as a technical advisor and build lasting customer relationships.
• Travel to customer sites, conferences, and other related events as required, and act as a public advocate for Google Cloud.
• Recommend integration strategies, enterprise architectures, platforms, and application infrastructure required to successfully implement a complete solution on Google Cloud.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 10 years of experience with cloud-native architecture in a customer-facing or support role, with application development and integration.
• 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 with presenting to technical stakeholders and executive leaders.
• Experience with coding in Python, JavaScript or TypeScript, Go, or Java, to demo, prototype, or workshop integration patterns with customers.

Preferred qualifications:

• Experience with Agentic frameworks such as LangGraph, Semantic Kernel, or the Google AI ADK.
• Experience in architecting and developing software or infrastructure for scalable, distributed systems.
• Experience with Parameter-Efficient Fine-Tuning (PEFT) recipes, including LoRA, and constructing custom training loops.
• Experience constructing evaluation harnesses to quantify model quality and agent performance (eval driven development).
• Experience in context engineering, including context caching strategies, multimodal analysis, JSON structured output generation, and function calling optimization.
• Experience in instrumenting AI code with observability tools to monitor agent performance and latency.