Principal Intelligent Automation Architect (AI & RPA)

Principal Intelligent Automation Architect (AI \& RPA)1

• *Key Responsibilities**

• Architectural Leadership: Lead the technical design of enterprise\-grade solutions using the Microsoft Power Platform and UiPath ecosystems.
• AI \& Agentic Workflows: Build and orchestrate AI Agents and LLM\-based workflows that go beyond simple "if\-then" logic to handle complex, non\-linear business processes.
• Document Intelligence: Implement advanced Document Understanding (UiPath) and AI Builder (Power Apps) models to automate the extraction and processing of semi\-structured and unstructured data.
• Custom Engineering: Use Python to extend platform capabilities, create custom APIs, and handle complex data manipulation that standard RPA tools cannot.
• App Development: Design high\-performance, user\-centric interfaces using Power Apps to act as the front\-end for automated workflows.
• Data Strategy: Manage complex data schemas and integration points using SQL Server, ensuring data integrity and high\-performance querying for automation logs and business data.
• Solution Governance: Oversee the full ALM (Application Lifecycle Management) process, ensuring robust deployment, security, and scalability of automation assets.

• *Technical Qualifications**

• RPA Platforms \- Expert\-level proficiency in UiPath (Studio, Orchestrator) and Power Automate (Cloud \& Desktop).
• AI \& ML \| Proven experience with UiPath Document Understanding, Power Platform AI Builder, and building AI Agents.
• Programming \| Advanced Python for automation, data science, or API integration.
• Database \| Strong SQL skills (Relational design, complex joins, stored procedures).
• Low\-Code \| Experience building Canvas and Model\-driven Power Apps.
• Architecture \| Experience as a Solution Architect overseeing infrastructure, security, and scalability.

• *Preferred Attributes**

• A "product mindset"—building tools that are resilient and user\-friendly, not just functional.
• The ability to explain complex AI concepts to non\-technical stakeholders.
• Experience in a Center of Excellence (CoE) environment.