Senior ML Architect, Applied Field Engineering

Snowflake is about empowering enterprises to achieve their full potential — and people too. With a culture that’s all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology — and careers — to the next level.

Our Applied Field Engineering organization is seeking an experienced customer facing AI ML Solutions Architect who can provide strategic and technical advisory, and hands-on expertise while working with technical decision makers and data scientists to design and architect AI ML solutions built on the Snowflake AI Data Cloud. This is a strategic role that works closely with cross-functional teams, including product, engineering, and the broader field organization to ensure successful execution and customer adoption of Snowflake’s AI & ML solutions.

• Be the technical expert in the room that positions Snowflake’s AI and ML features and value to technical stakeholders at Snowflake’s customers across the Americas.

• Partner with Snowflake account team teams and customer champions to scope and drive POCs to success and technical wins that prove the value of Snowflake’s capabilities, including executive readouts and business value cases.

• Build compelling AI ML demos and proof of concept (POCs) for customer

• Collaborate with Snowflake’s product and engineering teams to influence Snowflake’s AI and ML roadmaps based on customer feedback.

• Publish content that helps the team and company scale beyond your individual efforts, like blog posts, presentations at conferences, or technical collateral like notebooks and demos.

• Influence, tailor and maintain Sales Engineering AI and ML selling assets, including customer presentations, demonstrations, and customer stories.

• 5+ years of experience building and deploying machine learning and generative AI solutions in the cloud.

• MLOps experience on a major ML platform (e.g., Databricks, SageMaker): built shared pipelines/templates for teams and deployed/operated production models with monitoring and alerts; wrote unit/integration tests and used Git/CI.

• Hands-on scripting experience with Python, with experience using libraries such as Pandas, HuggingFace, XGBoost, PyTorch, TensorFlow, SciKit-Learn or similar.

• Have the following AI and ML Engineering skills:

  • Data Cleansing

  • Work with large datasets, and perform data quality evaluation/checks

  • Feature engineering

  • Determine relevant features for training and evaluation

  • Optimization of model performance/accuracy

  • MLOps and lifecycle management

• Strong skills presenting to both technical and executive audiences, whether impromptu on a whiteboard or using presentations and demos

• Bachelor’s Degree required, Masters Degree in computer science, engineering, mathematics or related fields, or equivalent experience preferred.

• Working knowledge of tools in the LLM ecosystem such as LangChain, LlamaIndex, and NeMo-Guard.

• Experience and understanding of large-scale infrastructure-as-a-service platforms (e.g. AWS, Microsoft Azure, GCP, etc.)

• 1+ years of practical Snowflake experience.

Every Snowflake employee is expected to follow the company’s confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties. It is every employee's duty to keep customer information secure and confidential.

Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.

How do you want to make your impact?

For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com