Sr AI Agent Engineer

Job Requirements Agent Data Engineer Primary Focus: Development, orchestration, and maintenance of the agentic framework that automates the engineering lifecycle. Secondary Focus: Technical execution and delivery of the Data Engineering lifecycle (Discover, Build, Test, Deploy). 6+ Years Experience AI Agent Engineer Key Responsibilities: Develop and refine agentic workflows using the Snowflake CoCo (CoCo) harness. Integrate LLMs, Cortex AI models, and orchestration logic to automate discovery, impact analysis, build, and testing stages. Build "Specification Driven Development" (SDD) capabilities, creating machine-readable instructions for data products. Collaborate with Data Engineers to convert manual engineering tasks into autonomous agentic patterns. Drive technical execution for data product delivery and datasets optimized for enterprise consumption. Oversee core engineering layer components, including SQL Stored Procedures, DBT models, and DataOps.Live. Maintain code quality and minimize defects while ensuring full alignment with EDO governance standards Contribute to automated engineering lifecycles—covering ingestion through deployment—via platform-agnostic frameworks. Essential Skills: AI/GenAI Engineering: Deep experience with Cortex Skills, LLM integration, and Retrieval-Augmented Generation (RAG) concepts. Agentic Frameworks: Proficiency in building and maintaining agentic workflows (e.g., CoCo CLI, Model Context Protocol). Foundational Knowledge: Solid background in data engineering (SQL, Python) to bridge the gap between AI capabilities and data platform requirements. Core Engineering Competencies: Advanced technical mastery of SQL, DBT models, and DataOps.Live (DOL) orchestration. Platform Expertise: Comprehensive proficiency across the Snowflake ecosystem and its integrated service offerings. Data Product Lifecycle: Strategic command of multi-tier architectures (Bronze, Silver, Gold) and the management of enterprise-grade data products.

Work Experience 7-10Years