Minimum qualifications:

　　Bachelor's degree in Computer Science, Data Science, or equivalent practical experience.

　　4 years of experience working in AI/ML as a technical sales engineer or in software engineering.

　　Experience with Python and ML frameworks (e.g., TensorFlow, PyTorch), and generative AI as a user or developer.

　　Experience delivering technical presentations and leading business value sessions.

　　Preferred qualifications: Experience in systems design with the ability to architect and explain data pipelines, ML pipelines, and ML training and serving approaches.

　　Strong understanding of search concepts, such as indexing, scoring, relevancy, faceting, and query rewriting.

　　Experience in full stack ML engineering (frontend: any; backend: Python) to seamlessly combine retrieval-based knowledge and generative text generation to implement and optimize RAG models using first-party and OSS models.

　　Experience with semantic search frameworks and tools/databases such as LangChain, Faiss, and Pinecone. Good understanding of nearest neighbor search concepts.

　　Familiarity with search products like ElasticSearch, Cognitive Search, Glean, etc.

　　About the job

　　As a Field Solutions Architect, you will support Google Cloud Sales and Engineering teams to incubate, pilot, and deploy Google Cloud's AI/ML and GenAI technology at AI native businesses and innovators, large enterprises, and early stage AI startups. You will help customers innovate faster with state of the art solutions using Google Cloud's flexible and open infrastructure including AI Accelerators (e.g. 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. Along the way, you will work closely with internal Cloud AI teams to remove roadblocks and shape the future of our offerings.

　　Google Cloud accelerates organizations' ability to digitally transform their business with the best infrastructure, platform, industry solutions and expertise. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology - all on the cleanest cloud in the industry. 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

　　Be a trusted advisor to key customers by understanding the customer's business process and goals. Architect AI-drive, spanning Data, AI, and Infrastructure, and work with peers to include the full cloud stack into overall architecture.

　　Demonstrate how Google Cloud is differentiated by working with customers on POCs, demonstrating features, tuning models, optimizing 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, etc. 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 closely with product and partner organizations on external enablement activities.

　　Travel as needed.