Facility: 1800 Parkway Center

Job Summary:

The AI Architect at Wellstar Health System is responsible for designing and implementing scalable AI architectures, ensuring AI solutions are integrated, secure, and aligned with Wellstar's IT and data infrastructure. This role will develop AI infrastructure strategies, establish MLOps best practices, and ensure AI models comply with governance, security, and interoperability standards. The AI Architect will collaborate with AI engineers, data scientists, IT teams, cloud architects, and compliance stakeholders to build AI-driven healthcare solutions that enhance clinical decision-making, operational efficiency, and patient outcomes. The ideal candidate will have deep expertise in AI system design, cloud computing, data pipelines, and model deployment and a strong understanding of security, risk management, and industry leading compliance frameworks.

Core Responsibilities and Essential Functions:

Design and implement scalable AI architectures, ensuring AI solutions integrate seamlessly with Wellstars IT, cloud, and data platforms. Develop and enforce MLOps best practices, including model versioning, automated retraining, and real-time AI monitoring. Implement secure AI model deployment pipelines with encryption, access controls, and adversarial robustness testing. Lead high-performing AI Technical team, collaborating with AI engineers, IT security teams, data scientists, and cloud architects to build scalable AI infrastructure and MLOps capabilities. Support AI Governance Council and AI COE in defining standards for responsible AI deployment and post-deployment monitoring. Performs other duties as assigned Complies with all Wellstar Health System policies, standards of work, and code of conduct.

Required Minimum Education:

• Bachelors Computer Science or Masters Artificial Intelligence-Preferred

Required Minimum License(s) and Certification(s):

All certifications are required upon hire unless otherwise stated.

Additional License(s) and Certification(s):

Required Minimum Experience:

Minimum 7 years experience in software architecture Required Minimum 3 years experience in AI architecture, MLOps, and AI model deployment Required Proven experience leading technical teams, including AI engineers, MLOps specialists, and data engineers, to design and implement enterprise-scale AI solutions. Required Extensive experience designing scalable AI infrastructure, integrating AI solutions with enterprise IT, and working with cloud-based AI platforms (AWS, Azure, Google Cloud AI). Required Strong knowledge of MLOps frameworks (ex. MLflow, Kubeflow, Vertex AI, AWS SageMaker, Azure ML). Required Hands-on experience in AI security and risk mitigation, including AI model monitoring, explainability, and bias detection Required Proficiency in containerization technologies (Docker, Kubernetes) and cloud-native computing environments. Required Experience working with large-scale healthcare data sources (EHR, imaging, genomics, claims data) and interoperability standards (FHIR, HL7, DICOM). Preferred Deep expertise in AI architecture patterns, distributed computing, and microservices for AI-driven applications. Preferred Experience with real-time AI processing and edge AI architectures for privacy-sensitive applications. Preferred Understanding of secure AI model deployment, federated learning, and privacy-preserving AI techniques. Preferred Strong programming skills in Python, Java, and AI model serving tools (TensorFlow Serving, TorchServe, ONNX Runtime). Preferred Strong ability to translate complex AI concepts into clear, strategic recommendations for non-technical stakeholders, including clinicians and executives. Preferred Strong understanding of regulatory compliance for AI solutions (HIPAA, FDA AI/ML, ISO 42001, NIST AI, HITrust). Preferred

Required Minimum Skills: