Field Solutions Architect, GenAI, Google Cloud

Minimum qualifications:

• Bachelor's degree in Computer Science, Data Science, or equivalent practical experience.
  
• 6 years of experience working in AI/ML as a technical sales engineer or in software engineering.
  
• Experience in Python and Machine learning frameworks (e.g., TensorFlow, PyTorch).
  
• Experience in Generative AI as a user or a developer.
  

Preferred qualifications:

• Experience in systems design, with the ability to architect and explain data pipelines, Machine Learning (ML) pipelines, and ML training and serving approaches.
  
• Experience with full-stack ML engineering to seamlessly combine retrieval-based knowledge and generative text generation to implement and optimize RAG models using first-party and OSS models.
  
• Experience with implementing search concepts, such as indexing, scoring, relevancy, faceting, and query rewriting and expansion.
  
• Experience with semantic search frameworks and tools/databases such as LangChain, Faiss, and Pinecone.
  
• Understanding of nearest neighbors search concepts.

As a Generative AI Field Solutions Architect, you will support Google Cloud Sales and Engineering teams to incubate, pilot, and deploy Google Cloud AI/ML and Generative AI (GenAI) technology with AI native customers, large enterprises, and early-stage AI startups. You will help customers innovate faster with solutions using Google Cloud’s flexible and open infrastructure including AI Accelerators (TPU/GPU).

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.

• Advise our customers by understanding the customer’s business process and objectives. Architect AI-drive, spanning Data, AI, and Infrastructure, and work with peers to include the full cloudstack into overall architecture.
  
• Work with customers, demonstrate features, tune models, optimize model performance, profiling, and benchmarking. Troubleshoot and find solutions to issues training/serving models in a large-scale environment.
  
• Build repeatable technical assets such as scripts, templates, reference architectures to enable other customers and internal teams. Work cross-functionally to influence Google Cloud strategy and product direction at the intersection of infrastructure and AI/ML by advocating for enterprise customer requirements.
  
• Coordinate regional field enablement with leadership and work with product and partner organizations on external enablement activities.
  
• Travel as needed.