AI/Automation Architect

Norman Regional Health System•Norman, OK

About The Position

The AI & Automation Architect is responsible for designing the end-to-end technical strategy for the organization's intelligent automation initiatives. This role involves selecting the right mix of technologies (RPA, Generative AI, Machine Learning, IDP) to solve complex business problems. The Architect ensures that solutions are scalable, secure, and cost-effective, moving beyond simple task automation to full-scale process transformation.

Requirements

Experience with OCR and data extraction tools.
Python
C#/.NET
PowerShell or Bash for infrastructure tasks.
Ability to design solutions that can handle spikes in volume.
Knowledge of load balancing and concurrent processing.
Understanding of RBAC (Role-Based Access Control).
Secure credential management.
Understanding of Virtual Machines (VMs), VDI (Virtual Desktop Infrastructure), and Docker/Kubernetes.
Setting up pipelines to automate the testing and deployment of bots.
Version control (Git) strategies for automation teams.
Bachelor’s degree in Computer Science, Data Science, or a related engineering discipline or equivalent experience required.
7+ years of IT experience, including at least 4 years designing scalable RPA and intelligent automation solutions using platforms like UiPath or Power Automate
Demonstrated expertise in integrating generative AI, machine learning models, and complex APIs into business workflows is essential
Proven leadership in guiding technical teams through the full software development lifecycle (SDLC) within an Agile environment is required
Experience translating business requirements into technical blueprints and managing stakeholder expectations is critical for success in this role

Nice To Haves

Holds an advanced professional certification in a major automation platform, such as the UiPath Certified Professional Automation Solutions Architect or Microsoft Power Platform Solution Architect

Responsibilities

Design scalable architectures for intelligent automation solutions, integrating RPA bots with AI models (e.g., using LLMs for decision-making within an RPA workflow)
Evaluate and select appropriate platforms and tools based on cost, security, and fit
Define how automation tools communicate with enterprise systems via APIs, webhooks, or database connections
Establish coding standards, reusable component libraries, and best practices for development teams to ensure consistency
Work with InfoSec to ensure all AI/automation workflows comply with data privacy laws (GDPR/CCPA/HIPAA) and internal security policies (e.g., PII masking, role-based access control)
Oversee the infrastructure sizing to support bots and high-volume AI inference requests
Collaborate with business analysts to determine if a process should be automated and how
Lead rapid Proof of Concept (PoC) projects to test emerging technologies before enterprise rollout
Design the "Control Room" strategy for monitoring bot health, AI model drift, and license utilization
Create failover strategies to ensure business-critical automations continue running during system outages (Disaster Recovery)
Meeting with Health system leaders to analyze a proposed process and determining if it requires simple RPA, complex AI (e.g., OCR/NLP), or if it shouldn't be automated at all
Drafting technical blueprints that detail exactly how a bot will log in, where data will be stored, how exceptions are handled, and which AI models will be called
Researching and testing new AI tools to see if they fit the health systems tech stack better than current tools
Designing and documenting the REST/SOAP API integrations between the automation platform and third-party apps
Calculating the necessary compute power (CPU/RAM/GPU) required for upcoming automations and provisioning Virtual Machines (VMs)
Configuring "Credential Vaults" so bots can log into systems without exposing passwords in the code
Building and maintaining a library of "snippets" that all developers must use to save time
Monitors bot health to ensure critical automations ran successfully overnight
Troubleshoot complex failures that regular support teams cannot fix
Planning and executing upgrades for the automation platform without breaking existing bots
Facilitating whiteboard sessions with non-technical departments to uncover their pain points
Prompt engineering for business logic, understanding "context windows," and mitigating AI hallucinations in enterprise data.