AI Engineer

Job Purpose:

The AI Engineer is responsible for developing, deploying, and managing artificial intelligence services across the organization, including local LLM infrastructure, agentic agents, Microsoft 365 Copilot solutions, and AI integrations with EMR and third-party vendor systems to enhance clinical workflows and operational efficiency.

Primary Duties and Responsibilities:

Local LLM Infrastructure Management

• Manage and optimize Open WebUI and Ollama deployment running on Nvidia GPUs in Proxmox LXC/Docker environment

• Configure and customize local LLM models for healthcare use cases ensuring HIPAA-compliant on-premises processing

• Monitor performance, manage model updates, and troubleshoot infrastructure issues

AI Agent Development & Deployment

• Build agentic agents and autonomous systems using both local (Ollama) and cloud-based LLMs

• Develop custom agents using Open WebUI for secure, on-premises patient data processing

• Create RAG (Retrieval-Augmented Generation) systems integrating local knowledge bases

• Design and implement custom Modelfiles for specialized healthcare tasks

• Implement Model Context Protocol (MCP) for standardized AI agent integration with enterprise systems

• Utilize Visual Studio Code with LLM integration extensions for AI-assisted development workflows

Microsoft 365 Integration

• Deploy and manage Copilot agents within M365 environment

• Build and configure solutions using Copilot Studio for custom AI experiences

• Integrate Copilot Studio with SharePoint, Teams, Exchange, and other Microsoft 365 services

• Configure custom actions, connectors, and data grounding

• Balance cloud-based M365 AI with on-premises local LLM capabilities

EMR & Vendor System Integration

• Coordinate AI deployment into EMR/EHR systems using HL7 FHIR standards

• Build secure pipelines between local Ollama models and clinical systems

• Partner with EMR vendors and third-party providers for AI implementations

• Develop custom integrations leveraging both local and vendor AI solutions

Security & Compliance

• Ensure HIPAA compliance for all AI implementations, especially local PHI processing

• Implement security controls for on-premises LLM systems to protect patient data

• Conduct risk assessments and maintain safety documentation

• Establish governance for local versus cloud AI data handling

Competencies:

• Service: We align our actions and decision making with the organization’s guiding platform keeping our patients at the center of all we do.

• Change Agent: We support and contribute to positive change in the organization.

• Communication and Interpersonal Relations: We communicate honestly and with compassion to build connections with our patients and each other.

• Effectiveness: We take ownership of work, doing the right thing for our customer and doing it well.

Organizational Culture:

All OrthoIllinois employees focus on service by putting the mission, vision, and value statements into practice and using the guiding principles of Compassion, Respect, Trust, Integrity, Innovation, Education Fiscal Responsibility, Practice Independence, Accountability, and Empowerment to direct their interactions and decision making. Employees promote and model the service standards to create lasting impressions, extraordinary moments, exceptional on-stage experiences, and meaningful and compassionate connections.

Education and Experience Requirements:

• At least two to four years of experience in AI/ML engineering or similar position and/or performing similar tasks

• Strong proficiency in Python programming for LLM integration, APIs, and automation

• Advanced working knowledge of Microsoft 365 and Google Suites

• Advanced working knowledge of cloud platforms (Azure preferred, AWS, or GCP)

• Working knowledge of:

• Ollama: model management, customization, quantization, and deployment

• Open WebUI: configuration, customization, and user interface development

• Docker and container orchestration (Docker, Kubernetes)

• Local LLM deployment and management (Ollama, llama.cpp, or similar)

• Model quantization, fine-tuning, and prompt engineering

• RAG systems, vector databases, and LLM orchestration

• Model Context Protocol (MCP): implementation for secure AI agent integration with enterprise systems

• Visual Studio Code: including LLM integration extensions and AI-assisted development tools

• Microsoft 365 Copilot and Copilot Studio: building custom AI agents and conversational experiences

• API development and microservices architecture

• EMR/EHR systems and healthcare interoperability (HL7 FHIR)

• HIPAA requirements and PHI handling

Preferred:

• Bachelor’s degree in computer science, Data Science, or related field from a four-year college or university

• Previous healthcare AI implementations

• Experience managing on-premises GPU infrastructure

• Portfolio demonstrating Ollama/local LLM projects

• Knowledge of TensorFlow, PyTorch, or similar ML frameworks

• Understanding of hybrid cloud/on-premises architectures

• Working knowledge of Proxmox, LXC containers, and GPU passthrough

• Working knowledge of Nvidia GPU optimization: CUDA, memory management for LLM workloads

• Working knowledge of Linux system administration

Environmental/Working Conditions:

Working environment is in an office/clinic setting. Requires consistent work outside of normal business hours, including evenings and weekends.

Physical/Mental Demands:

• Sit, stand, walk, stoop or kneel, crouch or crawl, and climb stairs for long periods of time

• Requires lifting, carrying and/or moving objects in a manner consistent with most office environments (generally, no more than 10 pounds on a frequent basis and 20 pounds on an occasional basis)

• Possess full range of motion in shoulders and arms, including reaching above or below the shoulder

• Finger dexterity; the ability to twist hands/wrists repetitively

• Vision (e.g., depth perception, color vision, strong vision up close), hearing (e.g., high pitch sounds, soft or distant sounds)

This description is intended to provide only basic guidelines for meeting job requirements. Responsibilities, knowledge, skills, abilities and working conditions may change as needs evolve.