Practice Customer Engineer I, Cloud AI, Google Cloud

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 expertise in our most strategic product areas, in partnership with Platform CEs, to be direct writing code to developing prototypes, proofs-of-concept, and demos to sell new, highly specialized solutions to customers. You will solve AI-centered 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 prowess, market knowledge, and direct technical engagement to prove the value of the Google Cloud portfolio.

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.

The US base salary range for this full-time position is $102,000-$146,000 + 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 complex workloads within Cloud AI to ensure successful adoption, primarily supporting the business cycle from technical evaluation through customer ramp.
• Combine sales strategies and direct 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.

Minimum qualifications:

• 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 engaging with, and presenting to, technical stakeholders and executive leaders.
• Experience with machine learning model development and deployment.
• Experience using programming languages (e.g., Python, JavaScript/TypeScript) to demo, prototype, or workshop with customers.
• Experience with AI agent orchestration frameworks (e.g., LangGraph, CrewAI, AutoGen), agentic design patterns (e.g., tool-use, multi-agent collaboration), and integrating large language models into autonomous workflows using advanced API prompting and Retrieval-Augmented Generation (RAG).

Preferred qualifications:

• Master's degree in Computer Science, Engineering, Mathematics, a technical field, or equivalent practical experience.
• Experience in building machine learning solutions and leveraging specific machine learning architectures (e.g. deep learning, LSTM, convolutional networks).
  
• Experience in architecting and developing software or infrastructure for scalable, distributed systems.
  
• Experience with frameworks for deep learning (e.g., PyTorch, Tensorflow, Jax, Ray, etc.), AI accelerators (e.g., TPUs, GPUs), model architectures (e.g., encoders, decoders, transformers), or using machine learning APIs.
• Ability to learn quickly, understand, and work with new emerging technologies, methodologies, and solutions in the cloud/IT technology space.