Field Solutions Architect, Generative AI, Google Cloud
Job Summary
Job Description
As a Generative AI Field Solutions Architect, you will support Google Cloud Sales and Engineering teams to incubate, pilot, and deploy Google Cloud’s Artificial Intelligence/Machine Learning 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). Responsibilities include advising customers, architecting AI-driven solutions spanning data, AI, and infrastructure, demonstrating features, optimizing model performance, troubleshooting issues, building technical assets, and working cross-functionally to influence Google Cloud strategy and product direction. You'll also coordinate regional field enablement and work with product and partner organizations on external enablement activities. Travel is required.
Must have:
- Bachelor's degree in CS/Data Science or equivalent
- 6+ years AI/ML experience (technical sales or software engineering)
- Python & ML framework experience (TensorFlow, PyTorch)
- Generative AI user/developer experience
Good to have:
- Systems design experience
- Full-stack ML engineering experience
- Experience with RAG models, semantic search, LangChain, Faiss, Pinecone
- Understanding of nearest neighbor search
5 skills required
Job Details
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 neighbor search concepts.
About the job
As a Generative AI Field Solutions Architect, you will support Google Cloud Sales and Engineering teams to incubate, pilot, and deploy Google Cloud’s Artificial Intelligence/Machine Learning 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).
In this role, you will identify, assess, and develop GenAI and Artificial Intelligence/Machine Leraning applications by applying key industry tools, techniques, and methodologies to solve problems. You will help customers leverage accelerators within their overall cloud strategy by helping run benchmarks for existing models, finding opportunities to use accelerators for new models, developing migration paths, and helping to analyze cost to performance. You will work with internal Cloud AI teams to remove roadblocks and shape the future of our offerings.
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
- 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 cloud stack into overall architecture.
- Work with customers, demonstrate features, tune models, optimize model performance, profiling, and bench marking. 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.
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