AI Consultant, Google Cloud Consulting

• Master's degree in Computer Science or Engineering.
  
• Experience in a statistical programming language (e.g., R or Python) and applied machine learning techniques (e.g., dimensionality reduction strategies, classification, and natural language processing frameworks).
  
• Experience designing and deploying with one or more of the following technologies: TensorFlow, Spark ML, CNTK, Torch, Caffe, Scikit-learn.
  
• Experience in systems design, with the ability to architect and explain data analytics pipelines and data flows.
  
• Excellent communication, written, presentation, and problem-solving skills.

About the job

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 engineered 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 an AI Consultant, you will work with Google's strategic customers on critical projects to help them transform their businesses using AI/ML technologies. You will help customers to develop, deploy, and manage custom AI solutions in production using Google Cloud technologies. You will provide technical project management, consulting, and technical aptitude to customer engagements while working with client executives and key technical leaders to deploy solutions.

In this role, you will work with key Google partners to scope engagements, manage programs, deliver consulting services, and provide technical guidance and best practice expertise. You will travel up to 30% of the time for client engagements.

Google Cloud accelerates organizations' ability to digitally transform their business with the best infrastructure, platform, industry solutions and expertise. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology - all on the cleanest cloud in the industry. 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.

The US base salary range for this full-time position is $171,000-$257,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

Responsibilities

• Work with customer technical leads, client executives, and partners to scope, manage and deliver implementations of cloud solutions becoming a trusted advisor to decision makers throughout the engagement.
  
• Work with internal specialists, product and engineering teams to package best practices and lessons learned into thought leadership, methodologies and published assets.
  
• Interact with sales, partners, and customer technical stakeholders to manage project scope, priorities, deliverables, risks/issues, and timelines for successful client outcomes.
  
• Advocate for customers needs to overcome adoption blockers and drive new feature development based on the field experience.
  
• Propose solution architectures and manage the deployment of cloud based machine learning solutions according to complex customer requirements and implementation best practices.