AI Engineer – Snowflake Cortex Agents Development

About the role

We are seeking an AI Engineer to design, build, and operationalize intelligent agents on Snowflake Cortex and Snowflake Intelligence. The role focuses on creating robust agent architectures, integrating them with enterprise data, and orchestrating prompts, tools, and workflows to address real business challenges.

Key responsibilities

• Develop and deploy agents using Snowflake AI features such as Cortex, Intelligence, and functions.
• Design prompts, tool‑calling logic, Retrieval‑Augmented Generation (RAG), evaluation metrics, and guardrails for LLM‑based agents.
• Utilize dbt within Snowflake to build modular models, document data, implement tests, and connect analytics to downstream agents and applications.
• Work with Snowflake semantic views or equivalent semantic‑layer concepts to provide consistent, business‑ready data for agents.
• Manage Snowflake environments, including roles, warehouses, performance tuning, secure data sharing, tasks, time travel, and semi‑structured data handling.
• Write advanced SQL queries with complex joins, window functions, CTEs, MERGE/upserts, and data quality checks.
• Apply Terraform or comparable IaC tools to provision Snowflake objects, environments, and access controls in a versioned manner.
• Develop Python code to integrate agents, APIs, and automation workflows across major cloud platforms.

Required profile

• 7–10 years of experience in AI/ML engineering, data engineering, or related fields.
• Hands‑on experience with Snowflake AI capabilities or a strong willingness to specialize in them.
• Proven track record designing and operating LLM‑based agents and prompt engineering.
• Solid foundation in data modeling, ELT processes, and CI/CD for data/analytics code.
• Experience working with at least one major cloud provider (AWS, Azure, or GCP) and managing APIs, identity, and secret management.

Required skills

• Snowflake AI features (Cortex, Intelligence, agents, functions)
• Large Language Models (LLMs) and agentic architectures
• SQL (complex joins, window functions, CTEs, MERGE)
• Data engineering fundamentals (data modeling, ELT, dbt)
• Terraform or equivalent Infrastructure‑as‑Code
• Python programming
• Major cloud platforms (AWS, Azure, or GCP)
• Snowflake semantic views (or similar semantic‑layer concepts)