Senior Technical Architect – AI/ML

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Snowflake

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 2012

☁️ SaaS

🏢 Enterprise

🤝 B2B

SaaS • Enterprise • B2B

Snowflake is a cloud-based data platform and data warehouse-as-a-service. It enables organizations to store, process, analyze, and share large volumes of structured and semi-structured data across multiple clouds, with separated scalable compute and storage, built-in security and data-sharing capabilities, and support for analytics and machine-learning workloads. Snowflake is delivered as a managed SaaS offering for enterprise customers and is primarily marketed and sold as a B2B product.

📋 Description

• Be a technical expert on all aspects of Snowflake in relation to the AI/ML workload • Build, deploy and ML pipelines using Snowflake features and/or Snowflake ecosystem partner tools based on customer requirements • Work hands-on where needed using SQL, Python, and APIs to build POCs that demonstrate implementation techniques and best practices on Snowflake technology for GenAI and ML workloads • Follow best practices, including ensuring knowledge transfer so that customers are properly enabled and are able to extend the capabilities of Snowflake on their own • Maintain deep understanding of competitive and complementary technologies and vendors within the AI/ML space, and how to position Snowflake in relation to them • Work with System Integrator consultants at a deep technical level to successfully position and deploy Snowflake in customer environments • Provide guidance on how to resolve customer-specific technical challenges • Support other members of the Services Delivery team develop their expertise • Collaborate with Product Management, Engineering, and Marketing to continuously improve Snowflake’s products and marketing

🎯 Requirements

• Minimum 10 years experience working with customers in a pre-sales or post-sales technical role • Skills presenting to both technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos • Thorough understanding of the complete Data Science life-cycle including feature engineering, model development, model deployment and model management. • Strong understanding of MLOps, coupled with technologies and methodologies for deploying and monitoring models • Experience and understanding of at least one public cloud platform (AWS, Azure or GCP) • Experience with at least one Data Science tool such as Sagemaker, AzureML, Vertex, Dataiku, DataRobot, H2O, and Jupyter Notebooks • Experience with Large Language Models, Retrieval and Agentic frameworks • Hands-on scripting experience with SQL and at least one of the following; Python, R, Java or Scala. • Experience with libraries such as Pandas, PyTorch, TensorFlow, SciKit-Learn or similar • University degree in computer science, engineering, mathematics or related fields, or equivalent experience

🏖️ Benefits

• Ability and flexibility to travel to work with customers on-site 25% of the time