Customer Engineer, Cloud AI, Financial Services

Customer Engineer, Cloud AI, Financial Services

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 Engineer (CE) 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 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 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.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 4 years of experience with cloud native architecture in industry or a customer-facing or support role.
• Experience engaging with, and presenting to, technical stakeholders and executive leaders.
• Experience with machine learning, model development and deployment.
• Experience with AI agent orchestration frameworks (e.g., LangGraph, CrewAI, AutoGen), agentic design patterns (e.g., tool-use, multi-agent collaboration), and integrating models into autonomous workflows via advanced API prompting and RAG.
• Experience leveraging programming or technical proficiencies to demo, prototype, or workshop with customers.

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.
• Ability to learn quickly, understand, and work with new emerging technologies, methodologies, and solutions in the cloud/IT technology space.

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.

Key skills/competency:

• Cloud Native Architecture
• Machine Learning
• AI Agent Orchestration
• Financial Services Expertise
• Technical Sales
• Customer Engagement
• Solution Prototyping
• API Integration
• Scalable Distributed Systems
• Product Feedback Loop