Job description

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) for Media and Entertainment 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 product areas, in partnership with Platform CEs, to be writing code to developing prototypes, proofs-of-concept, and demos to promote new, specialized solutions to customers. You will solve AI-centered customer issues and provide a critical feedback loop to influence product development. In this role, you will use your 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 business prowess, market knowledge, and technical engagement to prove the value of the Google Cloud portfolio. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $104000 - $151000 (USD) + 42.86% bonus target + equity + benefits Learn more about benefits at Google . • 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., LangGraph, CrewAI, AutoGen), agentic design patterns (e.g., tool-use, multi-agent collaboration), or integrating models into autonomous workflows via advanced API prompting or RAG. • Experience with machine learning model development and deployment. • Experience using programming languages to design demos, prototypes, or workshops for customers. • Experience engaging with, and presenting to, technical stakeholders and executive leaders. 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 with AI agent orchestration frameworks (e.g., LangGraph, CrewAI, AutoGen), agentic design patterns (e.g., tool-use, multi-agent collaboration), or integrating models into autonomous workflows via advanced API prompting or RAG. • Experience with machine learning model development and deployment. • Experience using programming languages to design demos, prototypes, or workshops for customers. • Experience engaging with, and presenting to, technical stakeholders and executive leaders. 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 developing and deploying Generative AI applications, with a focus on implementing RAG pipelines, integrating vector databases, and orchestrating LLM interactions via APIs. • Experience in building machine learning solutions and leveraging specific machine learning architectures (e.g., LLMs, Diffusion and Multimodal Models). • Familiarity with Media and Entertainment AI uses cases and workflows. • Ability to learn quickly, understand, and work with new emerging technologies, methodologies, and solutions in the cloud/IT technology space.

Requirements

Must-haves

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
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 machine learning model development and deployment.
Experience using programming languages to design demos, prototypes, or workshops for customers.
Experience engaging with, and presenting to, technical stakeholders and executive leaders.
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 machine learning model development and deployment.
Experience using programming languages to design demos, prototypes, or workshops for customers.
Experience engaging with, and presenting to, technical stakeholders and executive leaders.

Nice-to-haves

US: $104000 - $151000 (USD) + 42.86% bonus target + equity + benefits
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 developing and deploying Generative AI applications, with a focus on implementing RAG pipelines, integrating vector databases, and orchestrating LLM interactions via APIs.
Experience in building machine learning solutions and leveraging specific machine learning architectures (e.g., LLMs, Diffusion and Multimodal Models).
Familiarity with Media and Entertainment AI uses cases and workflows.
Ability to learn quickly, understand, and work with new emerging technologies, methodologies, and solutions in the cloud/IT technology space.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigoapimachine learningllm

Platform: google
Effort: medium