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Customer Developer, Cloud AI, Google Cloud

Google
Toronto, CAN
Senior · 6+ years experience
Onsite
CAD 162000-166000 / year
Discovered 1 weeks ago
pytorchtensorflow

Full Job Posting

Responsibilities

  • Drive the technical win for workloads within Cloud AI to ensure rapid and successful adoption, primarily supporting the sales 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 Developers 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 machine learning model development or deployment.
  • 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 engaging with, and presenting to, technical stakeholders and executive leaders.

Preferred qualifications:

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

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