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 Customer Engineer (CE) with a specialty in Cloud AI, Financial Services, 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 complex, specialized workloads. You will leverage your expertise in our product areas, in partnership with Platform CEs, to be writing code to developing prototypes, proofs-of-concept, and demos to promote 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 persuasively present practical and useful solutions on Google Cloud. You will blend sales prowess, market knowledge, and technical engagement to show 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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $152000 - $222000 (USD) + 42.86% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Drive the technical win for workloads within Cloud AI to ensure rapid and successful adoption, primarily supporting the sales cycle from technical evaluation through customer ramp.
- Combine sales strategies and development and prototyping to provide functional, customer-tailored solutions that secure buy-in from customer domain experts.
- Provide technical consultation to customers, acting as a technical advisor and building lasting customer relationships.
- Leverage learnings from customer engagements to contribute to reusable solutions and assets with the Go-To-Market team.
- Work within Product and Engineering management systems to document, prioritize and drive resolution of customer feature requests and issues.
Qualifications
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 10 years of experience with cloud native architecture in a customer-facing or support role.
- Experience with cloud engineering, on-premise engineering, virtualization, or containerization platforms.
- Experience engaging with, or presenting to, technical stakeholders or executive leaders.
- Experience in programming languages, debugging, systems design, prototyping, demos, or customer workshops.
Preferred qualifications:
- Master's degree in Computer Science, Engineering, Mathematics, a related technical field, or equivalent practical experience.
- Experience in building machine learning solutions and leveraging specific machine learning architectures (e.g., deep learning, long short-term memory (LSTM), convolutional networks).
- Experience in architecting and developing software or infrastructure for scalable, distributed systems.
- Experience with frameworks for deep learning (e.g., PyTorch, Tensorflow etc.), AI accelerators (e.g., TPUs), model architectures (e.g., encoders, decoders), or using machine learning APIs.
- Experience working within financial services industry.