- Snowflake fundamentals: SQL, warehouses, roles and access control, stages, and data modeling

- Data engineering: Ingestion with Snowpipe, streams and tasks, energetic tables, and transformations (dbt or Snowpark)

- Snowpark: Writing Python/Java/Scala code that runs inside Snowflake, including UDFs and stored procedures

- Cortex AI: Using built-in LLM functions (COMPLETE, SUMMARIZE, EXTRACT), embeddings, and Cortex Search for RAG

- Cortex Analyst and Agents: Natural-language-to-SQL over semantic models, and orchestrating tool-using agents

- Frontend: Streamlit in Snowflake for fast apps, or React/Next.js calling Snowflake via REST APIs or connectors

- Backend and integration: Python (FastAPI) services, Snowflake connectors, external functions, and API integrations

- Security and governance: RBAC, masking policies, row-level security, and keeping data inside Snowflakes boundary

- Cost and performance: Warehouse sizing, query optimization, caching, and credit monitoring

- Deployment: Git integration, Snowflake CLI, CI/CD, and environment promotion (dev/test/prod)

📌 AI Developer (Full Stack) (Bengaluru)
🏢 Axtria
📍 Bengaluru