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AI Solutions Architect

Stratedge IT Consulting INC

Job Location:

Austin, TX - USA

Monthly Salary: Not provided by the employer

Posted: 12 August 2026 (19 days ago)

Application Deadline: 9 November 2026

Vacancies: 1 Vacancy

Job Summary

Notes:

Only locals to Texas with a Texas ID will be accepted.

No relocation candidates will be considered.

Finalized candidates must show a Texas DL in a video meeting for screenshot capture as part of the compliance process.

State of TX

billing rate 65/hr on c2c 60 on w2

TxDOT has issued a request for an AI Solutions Architect to support Data Governance in building an enterprise agentic AI platform for governed data and AI workflows. The work is centered on architecting production multi-agent systems reusable AI agents Snowflake-based data solutions agent registries MCP integrations observability and human-in-the-loop controls. The ideal candidate has 10 years in software or data engineering has led a multi-agent platform running in production for at least 12 months and brings deep hands-on experience with Snowflake Python SQL agent evaluation LLM observability security controls and production-grade AI orchestration.

Responsibilities include (but are not limited to):

Architect the enterprise agentic AI platform: Design reusable task-specific agents and production multi-agent workflows with planner/worker orchestration tool calling state and memory management error recovery and an enterprise Agent Registry or Catalog.
Build governed AI infrastructure: Implement SSO RBAC MCP/OpenAI-compatible tool interfaces sandboxed agent-generated code execution approval workflows audit logging guardrails rollback procedures and human-in-the-loop controls.
Establish production operations and observability: Implement agent evaluation frameworks regression testing release quality gates per-run tracing token and cost monitoring failure analysis and integrations across Snowflake data engineering CI/CD and enterprise systems.

Minimum Candidate Characteristics:

10 years in software or data engineering including at least 3 years building LLM-based systems and 2 years operating agentic AI systems in production serving live business users or workloads; prototypes pilots demos and basic RAG chatbots do not qualify.
Must have served as lead architect for at least one multi-agent system operating in production for 12 months with direct ownership of orchestration tool calling state/memory management error recovery and an Agent Registry or Catalog.
Strong production experience with Python SQL Snowflake/data platforms CI/CD LLM evaluation and observability MCP or OpenAI-compatible interfaces SSO/SAML/OIDC RBAC guardrails sandboxed code execution and human approval workflows.

Exceptional Candidate Characteristics:

Experience with one or more Texas State Agencies.

Responsibilities:

Architect and establish the platforms 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.

Minimum (Required):

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)