AI Solutions Architect

AI Solutions Architect Location: Onsite (Austin, TX) - Candidates must be based in Austin, Texas or surrounding area within a 50-mile radius. Job asks to build and deploy user-facing agentic workflows tailored to enterprise needs and create task-specific AI agents that can be chained together to handle complex data lifecycle tasks. Highly preferred in a Candidate Characteristics: Experience with a Texas State Agency Deep, hands-on experience architecting complex data solutions within the Snowflake ecosystem (including Snowpark, Streamlit, and Cortex AI). Responsibilities: Architect and establish the platform's four foundational pillars: Reusable Foundational Agents: Design modular, task-specific AI agents that can be chained together to handle complex data lifecycle tasks. Enterprise Applications: Build and deploy user-facing agentic workflows tailored to enterprise needs. Balanced Agent Governance: Implement enterprise-grade security including Single Sign-On (SSO), Role-Based Access Control (RBAC), Model Context Protocol (MCP) or OpenAI-compatible standards, and a centralized Agent Catalog. Observability & Human-in-the-Loop Controls: Integrate comprehensive monitoring, logging, and guardrails to ensure reliability, transparency, and essential human oversight. Qualifications & Skills: Snowflake Mastery: Deep, hands-on experience architecting complex data solutions within the Snowflake ecosystem (including Snowpark, Streamlit, and Cortex AI). Agentic AI & LLMs: Proven track record of developing agentic frameworks, multi-agent orchestration, and leveraging open standards (MCP, OpenAI-compatible APIs). Data Engineering Infrastructure: Expertise in DBT, SQL, Python, and orchestrating modern ETL/ELT pipelines. Enterprise Security: Strong understanding of IAM, SSO, RBAC, and governance frameworks in public sector or highly regulated environments. Collaboration: Excellent communication skills to work closely with data engineers, architects, and business stakeholders. Years - Skills/Experience: 10 - Experience in software or data engineering 3 - Experience building LLM-based systems 2 - Experience designing and operating agentic AI systems in production — systems serving live business users or workloads. Prototypes, pilots, internal demos, and RAG chatbots do not meet this bar. Served as the lead architect of at least one multi-agent system that has run in production for 12+ months, with direct ownership of supervisory/planner–worker orchestration, tool calling, state and memory management, and error recovery for long-running workflows. Prior experience building Agent Registry or Catalog Production experience with agent-generated code that executes: sandboxed execution, automated validation and testing of generated artifacts, and engineer review-and-approve workflows gating deployment. (Directly relevant — this platform generates executable ingestion code and DBT packages.) Built and operated agent evaluation harnesses in production: offline eval suites, regression testing for prompt and model changes, and measurable quality gates that block release on failure. Operated LLM observability in production: per-run tracing of agent decisions and tool calls, token and cost monitoring, and hands-on triage of agent failures and incidents. Implemented guardrails and human-in-the-loop controls in a governed environment: approval gates, permission-scoped tool access for agents, audit logging, and rollback procedures Has built or deployed MCP servers/clients or OpenAI-compatible tool interfaces in a production system — not just consumed a vendor API. Strong Python and SQL; CI/CD for data platforms; SSO (SAML/OIDC) and RBAC design. Experience with Snowflake (Snowpark, Streamlit, Cortex AI) RIT Solutions