Snowflake Data Engineering

As a Snowflake Data Engineering professional, you will play a crucial role in designing and developing knowledge bases tailored for Agentic AI solutions. Your responsibilities will include implementing optimal ingestion mechanisms aligned with semantic search and Retrieval-Augmented Generation (RAG) goals using vector databases. You will be expected to apply prompt engineering techniques and utilize prompt libraries to enhance agent performance. Additionally, orchestrating multi-agent systems using MCP protocol and related frameworks will be a key aspect of your role. Collaboration with cross-functional teams to integrate ML Ops best practices like feature stores, feedback loops, and continuous model improvement will also be part of your responsibilities. Working closely with stakeholders to align AI capabilities with business use cases and outcomes is essential for success in this role. Qualifications Required: - Overall Experience of 5+ years - Proven experience in building knowledge bases for Agentic AI systems - Strong understanding of vector databases (e.g., FAISS, Pinecone, Weaviate) and semantic search strategies - Expertise in prompt engineering and familiarity with prompt libraries (e.g., LangChain, Guidance) - Hands-on experience with MCP protocol and agent orchestration frameworks - Solid background in ML Ops including feature store management, feedback loop design, and deployment pipelines - Familiarity with Snowflake and a preference for leveraging Cortex AI modules - Flexibility in technology stack with a strong foundation in Agentic AI principles and implementation This role as a Snowflake Data Engineering professional requires a deep understanding of AI technologies and a proactive approach to drive innovation in the field.,