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About the job
When leading companies choose Google Cloud, it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You listen and deliver what is most helpful for the customer. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.
As a Practice Customer Developer with a specialty in Cloud AI, you will partner with technical sales teams to differentiate Google Cloud to our customers. You will serve as a technical expert responsible for accelerating technical wins and adoption of specialized workloads. You will leverage your expertise in product areas, in partnership with Platform Customer Developers, to develop prototypes, proofs-of-concept, and demos to sell new, highly specialized solutions to customers. You will solve AI-centered customer issues and provide a critical feedback loop to influence product development.
You will have excellent organizational, communication, and presentation skills, engaging with customers to understand their business and technical requirements, and present solutions on Google Cloud. You will blend business prowess, market knowledge, and technical engagement to prove the value of the Google Cloud portfolio.
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 $105,000-$151,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. 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.
The Canada base salary range for this full-time position is CAD 132,000-135,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
Please note that the compensation details listed in Canada role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
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Drive the technical win for workloads within Cloud AI to ensure successful adoption, primarily supporting the business cycle from technical evaluation through customer ramp.
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Combine business strategies, development and prototyping to provide functional, customer-tailored solutions that secure buy-in from customer domain experts.
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Provide technical consultation to customers, acting as a technical advisor and building customer relationships.
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Leverage learnings from customer engagements to contribute to the solutions and assets with the Go-To-Market team.
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Work within product and engineering management systems to document, prioritize and drive resolution of customer feature requests and issues.
Minimum qualifications
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Bachelor's degree or equivalent practical experience.
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4 years of experience with cloud native architecture in industry or a customer-facing or support role.
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Experience with AI agent orchestration frameworks (e.g., Lang Graph, CrewAI, Auto Gen), agentic design patterns (e.g., tool-use, multi-agent collaboration), or integrating models into autonomous workflows via advanced API prompting or RAG.
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Experience with machine learning model development and deployment.
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Experience engaging with, and presenting to, technical stakeholders and executive leaders.
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Experience with programming or technical proficiencies to demo, prototype, or workshop with customers.
Preferred qualifications
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Master's degree in Computer Science, Engineering, Mathematics, a technical field, or equivalent practical experience.
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Experience in building machine learning solutions and leveraging specific machine learning architectures (e.g. deep learning, LSTM, convolutional networks).
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Ability to learn quickly, understand, and work with new emerging technologies, methodologies, and solutions in the cloud/IT technology space.
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Experience with frameworks for deep learning (e.g. Py Torch, Tensor Flow, Jax, Ray, etc.), AI accelerators (e.g. TPUs, GPUs), model architectures (e.g. encoders, decoders, transformers), or using machine learning APIs.
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Experience working with startups.
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About Google

Google specializes in internet-related services and products, including search, advertising, and software.
10,001+
Employees
Mountain View
Headquarters
$1,700B
Valuation
Reviews
3.7
25 reviews
Work-life balance
3.8
Compensation
4.2
Culture
3.4
Career
3.9
Management
2.8
68%
Recommend to a friend
Pros
Excellent compensation and benefits
Smart and talented colleagues
Great perks and work flexibility
Cons
Management and leadership issues
Bureaucracy and slow processes
Constantly changing priorities and reorganizations
Salary Ranges
57,502 data points
Junior/L3
L3
L4
L5
L6
L7
L8
Mid/L4
Principal/L7
Senior/L5
Staff/L6
Director
Junior/L3 · Data Scientist L3
0 reports
$176,704
total per year
Base
-
Stock
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Bonus
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$150,298
$203,110
Interview experience
9 interviews
Difficulty
3.4
/ 5
Duration
14-28 weeks
Offer rate
44%
Experience
Positive 0%
Neutral 56%
Negative 44%
Interview process
1
Application Review
2
Online Assessment/Technical Screen
3
Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
Common questions
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
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