Facility: 1800 Parkway Center

Job Summary:

The AI Engineer at Wellstar Health System is responsible for designing, developing, deploying, and maintaining AI-driven solutions that enhance clinical decision-making, operational efficiency, and patient engagement. This role will be part of the AI Operations Team in the AI Center of Excellence (COE) at Wellstar, collaborating closely with data scientists, software engineers, clinicians, IT teams, and AI governance stakeholders to implement machine learning (ML) models, optimize AI pipelines, and integrate AI solutions into Wellstar's healthcare systems. The AI Engineer will be responsible for end-to-end AI model lifecycle management, including data preprocessing, feature engineering, model training, evaluation, deployment, and monitoring.
The ideal candidate will have strong technical expertise in AI/ML development, software engineering skills, cloud-based AI deployment experience, and a deep understanding of MLOps practices.

Core Responsibilities and Essential Functions:

Work closely with AI Product Analysts, Data Scientists, Software Engineers, and Clinical Teams to implement AI solutions and ensure they align with organizational needs.
Work closely with DevOps and Cloud teams to deploy AI models into production environments, ensuring scalability, reliability, and performance.
Build and manage MLOps pipelines for automated model training, retraining, deployment, and monitoring.
Work with data engineers to ensure seamless AI model data ingestion and preprocessing.
Act as a technical liaison for 3rd party AI model assessments.
Communicate AI capabilities, limitations, and best practices to non-technical stakeholders.
Performs other duties as assigned
Complies with all Wellstar Health System policies, standards of work, and code of conduct.

Required Minimum Education:

• Bachelors Artificial Intelligence-Preferred
• Masters Artificial Intelligence-Preferred
• Doctorate 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 5 years experience in software engineering, process automation, or architecture. Required
Minimum 3 years experience in designing, developing, and deploying machine learning models and AI-driven applications. Required
Experience implementing AI/ML models in production environments (cloud, edge, or on-premise). Required
Strong programming expertise in Python and/or Java, with experience using AI/ML frameworks such as TensorFlow, PyTorch, Keras, Scikit-Learn, and XGBoost. Required
Hands-on experience with MLOps pipelines, model versioning, and CI/CD for AI deployment. Required
Familiarity with cloud-based AI platforms (AWS SageMaker, Azure ML, Google Vertex AI) and containerization technologies (Docker, Kubernetes). Required
Understanding of data engineering principles, including ETL processes, feature engineering, and data preprocessing for AI models. Required
Experience working with REST APIs, microservices, and AI model serving (e.g., FastAPI, Flask, TensorFlow Serving, TorchServe). Required
Experience working with healthcare data (EHRs, imaging, genomics, claims data, FHIR/HL7 standards). Preferred
Knowledge of edge AI and federated learning techniques for privacy-preserving AI models. Preferred
Hands-on experience in model explainability, bias detection, and fairness auditing (e.g., SHAP, LIME, Fairlearn, Aequitas). Preferred
Understanding of data privacy, security best practices, and AI regulatory compliance (HIPAA, FDA AI/ML guidelines, ISO/IEC 42001, NIST AI Risk Framework). Preferred
Familiarity with Graph Neural Networks (GNNs), NLP models (transformers, BERT, GPT), and computer vision techniques for medical imaging AI. Preferred
Strong knowledge of database management and query languages (SQL, NoSQL, BigQuery, Snowflake). Preferred

Required Minimum Skills: