Key skills
Cloud native architectureMachine learning deploymentAI agent orchestrationSoftware development for scalable systemsDeep learning frameworksMachine learning architecturesTechnical stakeholder engagementCommunication skillsPresentation skillsProblem-solving skillsGoAIMachine LearningDeep LearningPyTorchTensorFlowJAXLangGraphGoogle CloudPrototypingCollaborationSales
About this role
Google is a leading technology company, and they are seeking a Customer Engineer I specializing in Cloud AI for Healthcare and Life Sciences. This role involves partnering with technical sales teams to facilitate the adoption of Google Cloud products, providing technical consultation, and developing tailored solutions for customers.
Responsibilities:
- Drive the technical win for complex workloads within Cloud AI to ensure rapid and successful adoption, primarily supporting the business cycle from technical evaluation through customer ramp
- Combine sales strategies, development and prototyping to provide functional, customer-tailored solutions that secure buy-in from customer domain experts
- Provide deep 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
Requirements:
- 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 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 Retrieval-Augmented Generation (RAG)
- Experience engaging with, and presenting to, technical stakeholders and executive leaders
- Experience leveraging programming or technical proficiencies to demo, prototype, or workshop with customers
- Master's degree in Computer Science, Engineering, Mathematics, a 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, Jax, Ray, etc.), AI accelerators (e.g., TPUs, GPUs), model architectures (e.g., encoders, decoders, transformers), or using machine learning APIs
- Ability to learn quickly, understand, and work with new emerging technologies, methodologies, and solutions in the cloud/IT technology space