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Field Solutions Architect, Generative AI, Google Cloud
9 hours ago
Field Solutions Architect, Generative AI, Google Cloud
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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.
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:
In this role, you will support Google Cloud Sales and Engineering teams to incubate, pilot, and deploy Google Cloud’s 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).
You will identify, assess, and develop GenAI and AI/ML applications by applying key industry tools, techniques, and methodologies to solve issues. You will help customers leverage accelerators within the 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.
Responsibilities:
- Advise our customers by understanding the customer’s business process and objectives. Architect AI-driven solutions, 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 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.
Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law.
Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.
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Stockholm, Sweden Google Full timeField Solutions Architect, Generative AI, Google Cloud Apply 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,...
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