AI/ML Cloud Consultant, Professional Services, Google Cloud

Minimum qualifications:

• Bachelor's degree in Computer Science, Information Systems, related technical field, or equivalent practical experience.
• 3 years of experience project managing and delivering technical solutions.
  
• Experience in one or more discipline areas: machine learning, recommendation systems, natural language processing, computer vision, pattern recognition, or artificial intelligence.
  
• Experience with technical client service (e.g. consulting experience).
  

Preferred qualifications:

• Experience in systems design with ability to architect and explain data analytics pipelines and data flows.
  
• Experience in a statistical programming language like 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 from the following technologies: TensorFlow, Spark ML, CNTK, Torch, Caffe, Scikit-learn.
  
• Demonstrate excellent communication, written, presentation and problem solving skills.
  

About the job

The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google’s global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.

As an AI/ML Consultant, you will work directly with Google’s most strategic customers on critical projects to help them transform their businesses using AI/ML technologies. You will help our 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 on Google Cloud Platform.
You will also work closely with key Google partners currently servicing top accounts to scope engagements, manage programs, deliver consulting services, and provide technical guidance and best practice expertise.

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.

The US base salary range for this full-time position is $118,000-$174,000 + 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 successful implementations of Cloud AI solutions, becoming a trusted advisor to decision makers throughout the engagement.
  
• Propose solution architectures and manage the deployment of cloud based machine learning solutions according to complex customer requirements and implementation best practices.
  
• Work with internal specialists, Product, and Engineering teams to package approaches, 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 and issues, and timelines for successful client outcomes.
  
• Travel approximately 30% of the time for client engagements.