The Google Enterprise Services team leads the deployment of the world’s most advanced accelerated computing systems. We help global enterprises architect and implement massive-scale infrastructure across on-premise, hybrid and cloud environments.
In this role, you will bridge the gap between specialized hardware and enterprise software, shaping the future of large-scale distributed computing for the world’s largest organizations.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.
Responsibilities
- Work with customer technical leads, client executives, and partners to manage and deliver successful implementations of cloud solutions becoming a trusted advisor to decision makers throughout the engagement.
- Propose solution architectures and manage the deployment of cloud based distributed virtualized infrastructure solutions according to complex customer requirements and implementation best practices.
- Work with internal specialists, Product, and Engineering teams to package approaches, best practices, and lessons learned into thought leadership, methodologies, and published assets.
- Interact with Business, Partners, and customer technical stakeholders to manage project scope, priorities, deliverables, risks and issues, and timelines for successful client outcomes.
- Travel 30% of the time to customer sites and facilities.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 10 years of experience troubleshooting technical issues for internal/external partners or customers.
- 7 years of experience in customer management and stakeholder engagement for deployments.
- 5 years of experience with Artificial Intelligence/Machine Learning (AI/ML) Infrastructure (e.g., Graphics Processing Units (GPUs), Google Kubernetes Engine (GKE), Kubernetes, Kubeflow).
- Experience in either system design or reading code (e.g., Java, C++, Python).
Preferred qualifications:
- MBA or Master's degree in Computer Science, Engineering or a related field.
- Experience developing and tuning AI/ML models using frameworks such as JAX, PyTorch, or OpenXLA.
- Experience in one or more of the following areas: Developer Operations (DevOps), Security, Site Reliability Engineering (SRE).
- Experience in one or more of the following areas: Data Center Infrastructure, Networking, Compute, Storage.
- Customer-facing migration experience, including service discovery, assessment, planning, execution, and operations.