Customer Engineer, Cloud AI, Google Cloud

About the job

When leading companies choose Google Cloud, it is a 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 help make 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 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 most strategic product areas, in partnership with Platform CEs, to write code that develops prototypes, proofs‑of‑concept, and demos to promote new specialized solutions to customers.

You will solve AI‑centered customer challenges 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 $102,000–$146,000 plus bonus, equity, and benefits. Salary ranges are determined by role, level, and location. Individual pay is determined by work location and other 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.

By applying to this position you will have an opportunity to share your preferred working location from the following:
Atlanta, GA, USA;
Austin, TX, USA;
New York, NY, USA;
Reston, VA, USA
.

• Drive the technical win for complex workloads within Cloud AI to ensure rapid and successful adoption, primarily supporting the business cycle from technical evaluation through customer ramp.
• Combine business strategies and 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.

• Bachelor’s degree or equivalent practical experience.
• 4 years of experience with cloud native architecture in industry or a customer‑facing or support role.
• Experience with AI agent orchestration frameworks (e.g., Lang Graph, CrewAI, Auto Gen), 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.

• 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…