Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 10 years of experience in virtualization or cloud native architectures in a customer-facing or support role.
  
• Experience with big data and machine learning frameworks such as Tensorflow, PyTorch, or scikit-learn.
  
• Experience implementing MLOps at enterprise scale, on Google Cloud Platform or any other cloud.
  

Preferred qualifications:

• Experience building machine learning solutions, MLOps frameworks (Kubeflow), and leveraging specific machine learning architectures (e.g., deep learning, LSTM, convolutional networks, etc.).
  
• Experience building and deploying data and ML pipelines with a focus on automation.
  
• Familiarity with prompt tuning and experience delivering successful prototypes.
  
• Understanding a customer’s existing software workloads, and ability to define a technical migration roadmap to the Cloud reflecting customer needs.
  
• Familiarity with machine learning programming frameworks (e.g., LangChain, PyTorch, HuggingFace, and Tensorflow).
  
• Pre-sales/consulting skills, and ability to deliver technical presentations while leading detailed discovery and planning sessions that are aligned jointly with the customer.