Field Solutions Architect, Applied AI, Google Cloud

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Google Cloud is seeking a Field Solutions Architect for Generative AI in London onsite to incubate and deploy GenAI solutions for enterprise and early stage AI startups, leveraging TPU/GPU and Vertex AI. Candidates should have 6+ years in AI/ML as a technical sales engineer or software engineer, a CS or Data Science degree, strong Python and ML frameworks (TensorFlow, PyTorch), and hands on Generative AI experience. Preferred skills include full stack ML for RAG models, retrieval and semantic search with LangChain Faiss Pinecone, and ML pipeline design. To apply, tailor your resume to showcase customer-facing demos, benchmarks, cost/performance analysis, and cross-functional impact, quantify outcomes, and highlight collaboration with sales and product teams; be ready to discuss model optimization, deployment on Google Cloud, and travel willingness.

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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.
  
• Experience delivering technical presentations and leading business value sessions.

Preferred qualifications:

• 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.
  
• Experience in systems design, with the ability to architect and explain data pipelines, Machine Learning (ML) pipelines, and ML training and serving approaches.
  
• 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 AI/ML and Generative AI 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 Tensor Processing Unit/Graphics Processing Units (TPU/GPU).

In this role, you will identify, assess, and develop GenAI and AI/ML 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.

It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll succeed by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours.

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.