3 hours ago
Base Salary
$165k - $217k/yr
Responsibilities
- Serve as the technical expert positioning Snowflake AI and ML capabilities to customer technical stakeholders.
- Partner with account teams and customer champions to scope and deliver successful proofs of concept and technical wins.
- Collaborate with product and engineering teams to influence AI and ML roadmaps using customer feedback.
- Publish blogs, conference presentations, notebooks, demos, and other technical collateral.
- Create and maintain AI and ML sales engineering presentations, demonstrations, and customer stories.
Requirements
- 5+ years of experience building and deploying machine learning and generative AI solutions in the cloud.
- Knowledge of RAG, few-shot learning, prompt engineering, and fine-tuning for enterprise AI use cases.
- Deep Python knowledge and experience with common ML packages such as LangChain, pandas, scikit-learn, and PyTorch.
- Experience with data engineering tools including dbt, Airflow, and Spark.
- Strong presentation skills for technical and executive audiences, including whiteboarding sessions, formal readouts, and demos.
- Bachelor’s degree required; a master’s degree in computer science, engineering, mathematics, or a related field, or equivalent experience, is preferred.
- Experience with LLM ecosystem tools such as LangChain, LlamaIndex, or other open-source packages is preferred.
- Experience with large-scale infrastructure-as-a-service platforms such as AWS, Microsoft Azure, or GCP is preferred.
- At least one year of practical Snowflake experience is preferred.
- Knowledge of large-scale database technologies such as Snowflake, Netezza, Exadata, Teradata, or Greenplum is preferred.
Benefits
- The role supports customers across the Americas and is part of Snowflake’s Solution Engineering organization.
- Salary and benefits information is provided on the Snowflake Careers Site for United States jobs.
Tech Stack
Categories
Solutions Engineering
About Snowflake
Snowflake builds a cloud-native data platform used by enterprises to store, integrate, share, and analyze data across AWS, Azure, and Google Cloud. Its core products span data warehousing, data lakes, data engineering, and governed data sharing, sold via consumption-based subscriptions. Founded in 2012 and publicly traded on the NYSE (SNOW) following a 2020 IPO, Snowflake supports analytics and data application workloads across industries.