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.
Our Technical Solutions Engineers lead and own our large and important customer issues in addition to providing level two support to our other support teams. You will be a part of a global team that provides 24x7 support to help customers seamlessly make the switch to Google Cloud.
In this role, you will troubleshoot technical problems for customers with a mix of debugging, networking, system administration, updating documentation, and when needed, coding/scripting. You will make our products easier to adopt and use by making improvements to the product, tools, processes and documentation. Our Technical Solutions team is driven by customers and you will help drive the success of Google Cloud by understanding and advocating for our customers’ issues. 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
- Troubleshoot and resolve highly technical issues across the Google Cloud AI/ML portfolio, focusing on customer-reported , deployment failures, model performance degradation and infrastructure-related problems.
- Work directly with customers on their ML deployments (including Generative AI models)to ensure production readiness,high availability.
- Utilize coding and scripting skills (primarily Python) to read,debug, and reproduce customer issues within their ML models (TensorFlow, PyTorch) or deployment environments(Kubernetes, Compute Engine).
- Manage customer problems through effective diagnosis,clear documentation and the development/implementation of new investigation tools to increase diagnostic speed.
- Develop an in-depth understanding of Google Cloud's AI/ML solutions and share this knowledge to upskill the wider global support organization. Participate in an on-call rotation, may include working non-standard hours,nights,or weekends as part of our global 24/7 support model.
Qualifications
Minimum qualifications:
- Bachelor's degree in Computer Science, Engineering, Mathematics, a related technical field, or equivalent practical experience.
- 5 years of experience in a technical role such as Technical Support, Software Engineering, or Solutions Engineering.
- Experience coding in one or more general purpose languages (e.g., Python, Java, Go, C or C++) including data structures, algorithms, and software design.
- Experience with Artificial Intelligence (AI) concepts and Machine Learning (ML) techniques.
- Experience with computer networking (e.g., TCP/IP, DNS, Load Balancing, routing) and Linux/Unix system administration.
Preferred qualifications:
- Professional-level certification on Google Cloud, such as the Professional Machine Learning Engineer or Professional Cloud Architect.
- Experience with Google Cloud's AI/ML product portfolio, including Vertex AI (Vertex AI Workbench, Pipelines, Endpoints, TensorBoard) and Generative AI tools (Gemini, Gen AI Studio).
- Experience in specialized ML areas like Natural Language Processing (NLP), Computer Vision, or Recommendation System.
- Experience with public cloud infrastructure and core services (e.g., Compute Engine, Cloud Storage,BigQuery).
- Knowledge of ML frameworks such as TensorFlow, Keras, or PyTorch.
- Ability to lead the design and implementation of AI-based solutions or debugging tools, demonstrating strong collaborating skills.