Job description 

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, along with implementing Machine Learning Operations at enterprise scale, on any cloud platform.
   
• Experience engaging with, and presenting to, technical stakeholders and executive leaders.
   

Preferred qualifications:

• Experience in building and deploying data and ML pipelines with a focus on automation.
   
• Experience with building Machine Learning (ML) Solutions, Machine Learning Operations frameworks like kubeflow, and leveraging specific machine learning architectures (e.g., deep learning, LSTM, convolutional networks, etc.).
   
• Experience in understanding a complex customer’s existing software workloads and the ability to define a technical migration roadmap to the Cloud reflecting specific customer needs.
   
• Familiarity with machine learning programming frameworks such as LangChain, PyTorch, HuggingFace, and Tensorflow.
   
• Familiarity with prompt tuning and experience delivering successful prototypes.

Responsibilities

• Work with the team to identify and qualify business opportunities, understand key customer technical objections and develop the strategy to resolve technical blockers.
• Provide machine learning expertise to support the technical relationship with Google’s customers, including product and solution briefings, proof-of-concept work, and partner directly with product management to prioritize solutions impacting customer adoption to Google Cloud.
• Recommend integration strategies, enterprise architectures, platforms and application infrastructure required to implement a complete solution using best practices on Google Cloud.
• Travel to customer sites, conferences, and other related events as needed.