Cloud AI Engineer (google)

Qualifications

Minimum qualifications:

• Bachelor's degree in Computer Science or equivalent practical experience.
  
• 4 years of experience building machine learning solutions and working with technical customers.
  
• Experience designing cloud enterprise solutions and supporting customer projects to completion.
  
• Experience coding in one or more general purpose languages (e.g., Python, Java, Go, C or C++) including data structures, algorithms, and software design.
  

Preferred qualifications:

• Experience working with recommendation engines, data pipelines, or distributed machine learning, and experience with data analytics, data visualization techniques and software, and deep learning frameworks.
  
• Experience in software development, professional services, solution engineering, and technical consulting, with expertise in architecting and rolling out new technology and solution initiatives.
  
• Experience with core Data Science techniques.
  
• Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ELT and reporting/analytic tools and environments.
  
• Knowledge of cloud computing, including virtualization, hosted services, multi-tenant cloud infrastructures, storage systems, and content delivery networks
  
• Excellent customer-facing communication skills.
  

Summary

• Bachelor's degree in Computer Science or equivalent practical experience.
  
• 4 years of experience building machine learning solutions and working with technical customers.
  
• Experience designing cloud enterprise solutions and supporting customer projects to completion.
  
• Experience coding in one or more general purpose languages (e.g., Python, Java, Go, C or C++) including data structures, algorithms, and software design.
  

Description

The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.

As a Cloud AI Engineer, you'll play a key role in ensuring that strategic customers have the best experience moving to the Google Cloud machine learning (ML) suite of products. You will design and implement machine learning solutions for customer use cases, leveraging core Google products. You'll work with customers to identify opportunities to transform their business with machine learning, and will travel to customer sites to deploy solutions and deliver workshops designed to educate and empower customers to realize the full potential of Google Cloud. You will have access to Google’s technology to monitor application performance, debug and troubleshoot product code, and address customer and partner needs. In this role, you will lead the timely execution of adopting the Google Cloud Platform solutions to the customer’s requirements.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities

• Be a trusted technical advisor to customers and solve complex machine learning challenges.
  
• Coach customers on the practical challenges in machine learning systems: feature extraction and feature definition, data validation, monitoring, and management of features and models.
  
• Work with Customers, Partners, and Google Product teams to deliver tailored solutions into production.
  
• Create and deliver best practice recommendations, tutorials, blog articles, and sample code.
  
• Travel up to 30% for in-region for meetings, technical reviews, and onsite delivery activities.
  

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