Customer Engineer, Cloud AI, Financial Services, Google Cloud

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.
• - 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.

Minimum qualifications:

Preferred qualifications:

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.