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. As a Practice Customer Engineer, Cloud 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.

This role involves leveraging deep expertise in Google Cloud's AI product areas, in partnership with Platform Customer Engineers, to write code for developing prototypes, proofs-of-concept (POCs), and demos. You will solve AI customer issues, provide critical feedback to influence product development, and engage with customers to understand their business and technical requirements. The position blends 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.

Responsibilities

• Drive the technical win for workloads within Cloud AI, ensuring 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 AI
• Generative AI
• Machine Learning
• Cloud Native Architecture
• RAG pipelines
• LLMs
• API Prompting
• Vector Databases
• Customer Engagement
• Technical Sales