Job Description
We are seeking a highly skilled AI Agent Solution Architect to lead the design, implementation, and deployment of advanced enterprise AI solutions. In this role, you will focus on building semantic data models, configuring Snowflake AI Agents across multiple business domains, and integrating Snowflake CoWork to drive organizational efficiency.
The ideal candidate bridges the gap between complex data architecture and cutting-edge artificial intelligence, ensuring that our AI agents are deeply integrated, highly validated, and ready for production success.
Architecture & Semantic Modeling
AI Agent Solution Architecture: Design scalable, secure, and robust end-to-end architectures for domain-specific AI agents within our enterprise data ecosystem.
Semantic Data Modeling: Define, build, and maintain standard Semantic Data Models and comprehensive Business Glossaries to ensure AI agents precisely understand and contextually interpret organizational data.
Implementation & Configuration
Snowflake AI Agent Configuration: Configure, fine-tune, and optimize Snowflake AI Agents across diverse business domains (e.g., Finance, HR, Supply Chain, Operations).
Snowflake CoWork Integration: Set up, configure, and seamlessly embed Snowflake CoWork features into existing team workflows to maximize collaborative AI capabilities.
Quality Assurance & Deployment
Validation & UAT: Author comprehensive Validation Documentation and actively support business stakeholders during User Acceptance Testing (UAT) to ensure AI agents meet strict business logic and accuracy requirements.
Production Rollout: Lead Production Deployment initiatives and write detailed Runbooks to guarantee smooth operational transitions.
Knowledge Enablement & Support
Training & KT: Create high-quality Training Materials and lead Knowledge Transfer (KT) sessions to empower internal technical teams and end-users.
Hypercare Support: Provide dedicated Hypercare Support during the initial rollout phase, rapidly triaging anomalies and optimizing performance.
Required Qualifications & Skills
Snowflake Expertise: Proven experience architectural mapping inside Snowflake, specifically leveraging its native AI/ML capabilities, Cortex features, and data clean rooms.
AI/LLM Engineering: Deep understanding of Retrieval-Augmented Generation (RAG), prompt engineering, vector databases, and semantic search frameworks.
Data Modeling: Strong background in enterprise data warehousing, semantic layer modeling, and establishing governance via business glossaries.
DevOps & Operations: Experience writing production runbooks, managing CI/CD pipelines for data/AI products, and structuring UAT validation frameworks.
Communication: Exceptional ability to translate complex AI behaviors into clear concepts for business users during UAT and training.