Data Scientist With Snowflake-Offshore

Job Description: We are seeking an experienced Snowflake Data Scientist with deep expertise in building, deploying, and optimizing Large Language Models (LLMs) and advanced AI workloads on the Snowflake Data Cloud.

• Responsibilities: 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.

• Qualifications: 2-4 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.