Design, build, and deploy LLM-based solutions on Snowflake, leveraging Cortex AI functions (e.g., Snowflake LLM inference, fine-tuning, embeddings).
Create custom LLM pipelines using Snowpark ML and external models integrated through Snowflake.
Build and operationalize machine learning models, including NLP, predictive modeling, generative AI, and recommendation systems.
Develop and apply prompt engineering, RAG pipelines, and semantic search patterns using Snowflake features and Vector Embeddings.
Conduct exploratory data analysis (EDA), feature engineering, and statistical modeling using Snowflake & Snowpark Python.
Deploy, monitor, and optimize model performance directly within the Snowflake environment.
Integrate Snowflake ML solutions with cloud platforms (GCP) and downstream applications.
Work with business stakeholders, product teams, and analytics leaders to identify opportunities for AI-driven transformation.
Deliver high-quality, interpretable insights and communicate complex technical results to non-technical audiences.
Partner with data engineers, architects, and analysts to build end-to-end Snowflake-native AI solutions.

3-6 years of experience in Data Science, Machine Learning, or Applied AI roles.
Hands-on expertise with Snowflake, including Snowpark Python, UDFs, Warehouses, Tasks, Streams, and performance optimization.
Strong experience with LLMs, including fine-tuning, embeddings, prompt engineering, evaluation, and inference.
Proven experience using Snowflake Cortex AI or similar cloud LLM platforms.
Proficiency in Python, SQL, and ML frameworks (scikit-learn, transformers, LangChain, etc.).
Familiarity with cloud platforms like GCP and integrating it with SnowFlake.
Experience with NLP techniques, vector databases, and semantic search.
Familiarity with MLOps workflows: model deployment, monitoring, CI/CD pipelines.
Strong understanding of data governance, security, and ML best practices.

Data Scientist, SnowFlake

Key skills