Enterprise Agentic AI Architect - Snowflake - BizTech Fusion

Enterprise Agentic AI Architect – Snowflake

Biz Tech Fusion is hiring for one of our clients and is seeking a highly experienced Enterprise Agentic AI Architect / Cloud Solutions Architect to lead the architecture and delivery of enterprise-scale Agentic AI solutions.

Location:

Austin, TX

Work Arrangement:
Onsite, 5 days/week

Experience:

14–18+ years overall IT experience

Work Authorization:
Must be authorized to work in the U.S.

Interview:
Face-to-face

Snowflake Ecosystem

• Snowflake
• Snowpark
• Streamlit
• Cortex AI

• LLM
• RAG
• Agentic AI
• Multi-agent architectures
• MCP / Model Context Protocol
• OpenAI-compatible tool interfaces
• Agent orchestration and tool calling

• Python
• SQL
• DBT
• ETL/ELT
• CI/CD for data platforms

• 10+ years of experience in software or data engineering.
• 3+ years of hands-on experience building LLM-based systems.
• 2+ years designing and operating Agentic AI systems in production.
• Proven experience architecting and leading multi-agent systems running in production for 12+ months.
• Strong experience with supervisory/planner-worker architectures, tool calling, state management, memory, and workflow recovery.
• Experience building an Agent Registry or Agent Catalog.
• Hands-on experience deploying MCP servers/clients or OpenAI-compatible tool interfaces in production.
• Experience with agent-generated executable code, including sandboxing, automated validation/testing, and approval workflows.
• Experience building AI/agent evaluation frameworks, regression testing, and production quality gates.
• Production experience with LLM observability, including tracing, token/cost monitoring, and incident troubleshooting.
• Experience implementing AI guardrails, human-in-the-loop workflows, audit logging, permission controls, and rollback processes.
• Strong understanding of SSO, SAML/OIDC, RBAC, IAM, security, and enterprise governance.

• Architect reusable, task-specific AI agents for complex enterprise workflows.
• Design and implement enterprise multi-agent applications.
• Establish agent governance, security, and centralized agent catalogs.
• Build observability and monitoring frameworks for production AI systems.
• Implement human approval and intervention mechanisms.
• Partner with data engineers, architects, developers, and business stakeholders.
• Drive production adoption of scalable and governed Agentic AI solutions.