Customer Engineer, Cloud Ai, Media and Entertainment

Google Google · Big Tech · Los Angeles, CA +1

Customer Engineer specializing in Cloud AI for the Media and Entertainment industry. This role involves partnering with technical sales teams to drive adoption of Google Cloud's AI solutions, acting as a technical expert to build prototypes, proofs-of-concept, and demos. The engineer will solve AI-centered customer issues, provide feedback to product development, and engage with customers to understand and present solutions.

What you'd actually do

  1. 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.
  2. Combine business strategies and development and prototyping to provide functional, customer-tailored solutions that secure buy-in from customer domain experts.
  3. Provide deep technical consultation to customers, acting as a technical advisor and building lasting customer relationships.
  4. Leverage learnings from customer engagements to contribute to reusable solutions and assets with the Go-To-Market team.
  5. Work within product and engineering management systems to document, prioritize and drive resolution of customer feature requests and issues.

Skills

Required

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

Nice to have

  • architecting and developing software or infrastructure for scalable, distributed systems
  • developing and deploying Generative AI applications
  • focus on implementing RAG pipelines
  • integrating vector databases
  • orchestrating LLM interactions via APIs
  • building machine learning solutions
  • leveraging specific machine learning architectures (e.g., LLMs, Diffusion and Multimodal Models)
  • Media and Entertainment AI uses cases and workflows
  • learn quickly, understand, and work with new emerging technologies, methodologies, and solutions in the cloud/IT technology space

What the JD emphasized

  • AI agent orchestration frameworks
  • agentic design patterns
  • integrating models into autonomous workflows
  • machine learning model development and deployment
  • Generative AI applications
  • implementing RAG pipelines
  • integrating vector databases
  • orchestrating LLM interactions via APIs
  • building machine learning solutions
  • specific machine learning architectures

Other signals

  • customer-facing technical expert
  • accelerating adoption of complex workloads
  • writing code to develop prototypes, proofs-of-concept, and demos
  • solve AI-centered customer issues
  • feedback loop to influence product development