Customer Engineer, Cloud AI

About The Job

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 Cloud Artificial Intelligence (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 product areas, in partnership with Platform Customer Engineers (CEs), to be writing code to develop prototypes, proofs-of-concept (POCs), and demos to promote new specialized solutions to customers. You will solve AI 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 persuasively present practical and useful solutions on Google Cloud. You will blend sales expertise, market knowledge, and technical engagement to prove the value of the Google Cloud portfolio.

The US base salary range for this full-time position is $125,000-$183,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

• Drive the technical win for workloads within Cloud AI to ensure rapid and successful adoption, primarily supporting the business cycle from technical evaluation through customer ramp.
• Combine business strategies, 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 (GTM) team.
• Work within product and engineering management systems to document, prioritize, and drive resolution of customer feature requests and issues.

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 with AI agent orchestration frameworks (e.g., LangGraph, CrewAI, AutoGen), agentic design patterns (e.g., tool-use, multi-agent collaboration), and integrating models into autonomous workflows via advanced API prompting and RAG.
• Experience with machine learning model development and deployment.
• Experience engaging with, and presenting to, technical stakeholders and executive leaders.
• Experience using programming languages to design demos, prototypes, or workshops for customers.

Preferred Qualifications

• Master's degree in Computer Science, Engineering, Mathematics, a technical field, or equivalent practical experience.
• Experience in architecting and developing software or infrastructure for scalable, distributed systems.
• Experience in building machine learning solutions and leveraging specific machine learning architectures (e.g., LLMs, Diffusion, and Multimodal Models).
• Experience developing and deploying Generative AI applications, with a focus on implementing RAG pipelines, integrating vector databases, and orchestrating LLM interactions via APIs.
• Experience in the manufacturing or energy industry.
• Ability to learn quickly, understand, and work with new emerging technologies, methodologies, and solutions in the cloud/IT technology space.

Key skills/competency

• Cloud Architecture
• AI Agent Orchestration
• Machine Learning Deployment
• Generative AI
• RAG Pipelines
• Vector Databases
• API Integration
• Customer Engagement
• Technical Sales
• Solution Prototyping