FAQs
What location options are available for this position?
The position offers preferred working locations in Madrid, Spain; Milan, Metropolitan City of Milan, Italy; and Rome, Metropolitan City of Rome Capital, Italy.
What are the minimum qualifications required for this role?
The minimum qualifications include a Bachelor's degree in Computer Science, Data Science, or equivalent practical experience, 6 years of experience in AI/ML as a technical sales engineer or software engineering, proficiency in Python and Machine Learning frameworks (e.g., TensorFlow, PyTorch), experience in Generative AI, and the ability to communicate fluently in Spanish or Italian.
What are some of the preferred qualifications for this role?
Preferred qualifications include experience in systems design, full-stack ML engineering, implementing search concepts, and experience with semantic search frameworks and tools/databases such as LangChain, Faiss, and Pinecone.
What responsibilities does the Generative AI Field Solutions Architect have?
Responsibilities include advising customers, architecting AI-driven solutions, demonstrating features, optimizing model performance, building repeatable technical assets, and coordinating regional field enablement activities.
Is travel required for this position?
Yes, travel is required as needed.
How does this role interact with customers?
In this role, you will advise customers by understanding their business processes and objectives, demonstrate features, tune models, and troubleshoot issues in a large-scale environment.
Does Google support applicants with disabilities?
Yes, Google is committed to an inclusive workplace and invites applicants with disabilities to apply, providing accommodations as needed.
What technologies does this position focus on?
This position focuses on Google Cloud’s AI/ML and Generative AI technology, including AI Accelerators (TPU/GPU) and various ML frameworks.
Will the Field Solutions Architect work cross-functionally?
Yes, the Field Solutions Architect will work cross-functionally to influence Google Cloud strategy and product direction and collaborate with internal Cloud AI teams.
What is the role of technical assets in this position?
Building repeatable technical assets such as scripts, templates, and reference architectures is crucial to enable other customers and internal teams efficiently.