AI Engineer, Atlanta

Job Title: AI Engineer

Job Location: Atlanta, GA

Workplace Type: Remote (requiring annual visit to office) 

Workplace Schedule: Monday through Friday 
Job Summary:

The AI Engineer is responsible for designing, developing, deploying, and maintaining AI-driven solutions that enhance clinical decision-making, operational efficiency, and user engagement. This role is part of an AI Operations Team, 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 enterprise systems. The AI Engineer will manage the full AI model lifecycle, including data preprocessing, feature engineering, model training, evaluation, deployment, and monitoring.

The ideal candidate will bring strong technical expertise in AI/ML development, robust software engineering skills, cloud-based AI deployment experience, and a deep understanding of MLOps practices.

Core Responsibilities and Essential Functions:

Collaborate with AI Product Analysts, Data Scientists, Software Engineers, and Clinical or Business Teams to implement AI solutions aligned with organizational needs

Partner 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 third-party AI model assessments

Communicate AI capabilities, limitations, and best practices to non-technical stakeholders

Perform other duties as assigned

Comply with all organizational policies, standards of work, and code of conduct

Required Minimum Education:

Bachelor's in Artificial Intelligence or related field – Preferred

Master's or Doctorate in Artificial Intelligence or related field – Preferred

Required Minimum Experience:

Minimum 5 years of experience in software engineering, process automation, or architecture

Minimum 3 years of experience designing, developing, and deploying machine learning models and AI-driven applications

Proven experience implementing AI/ML models in production environments (cloud, edge, or on-premise)

Strong programming expertise in Python and/or Java, with experience using AI/ML frameworks such as TensorFlow, PyTorch, Keras, Scikit-learn, and XGBoost

Hands-on experience with MLOps pipelines, model versioning, and CI/CD for AI deployment

Familiarity with cloud-based AI platforms (AWS SageMaker, Azure ML, Google Vertex AI) and containerization technologies (Docker, Kubernetes)

Understanding of data engineering principles, including ETL processes, feature engineering, and data preprocessing for AI models

Experience working with REST APIs, microservices, and AI model serving (e.g., FastAPI, Flask, TensorFlow Serving, TorchServe)

Preferred Experience:

Experience working with healthcare data (EHRs, imaging, genomics, claims data, FHIR/HL7 standards)

Knowledge of edge AI and federated learning techniques for privacy-preserving AI models

Experience in model explainability, bias detection, and fairness auditing (e.g., SHAP, LIME, Fairlearn, Aequitas)

Understanding of data privacy, security best practices, and AI regulatory compliance

Familiarity with Graph Neural Networks (GNNs), NLP models (Transformers, BERT, GPT), and computer vision techniques for medical imaging AI

Strong knowledge of database management and query languages (SQL, NoSQL, BigQuery, Snowflake)