Expert AI/ML Engineer

Job Description Machine Learning Engineer - AI/ML Team

Location: Alpharetta, GA (Hybrid – On-site & Remote)

Engineering & Technology

Responsibilities

• Design, build, train, validate, and deploy machine learning models for business and healthcare analytics use cases.
• Support predictive modeling, classification, forecasting, anomaly detection, NLP, document intelligence, and related AI solutions.
• Perform exploratory data analysis, feature engineering, model selection, and performance tuning.
• Evaluate model outputs and recommend enhancements to accuracy, stability, fairness, explainability, and reliability.
• Troubleshoot data quality, model drift, inconsistent outputs, and production performance issues.
• Partner with data engineering, analytics, and business teams to ensure models are built on trusted, governed data.
• Mentor team members and provide guidance across model design, development, testing, validation, and deployment.
• Review model architecture, code, features, and evaluation methods to support technical quality and production readiness.
• Help define reusable standards, templates, checklists, and best practices for AI/ML delivery.
• Contribute to MLOps practices: experiment tracking, versioning, automated testing, CI/CD, deployment, monitoring.
• Support model promotion workflows, retraining strategies, rollback planning, alerting, and production support.
• Assist in identifying and shaping AI use cases across reporting, operations, governance, and automation.
• Provide technical guidance for GenAI/LLM solutions (RAG, semantic search, text-to-SQL, summarization, document processing).
• Support governance and compliance activities: documentation, validation, risk review, auditability, responsible AI.

Required Skills

• 8+ years in machine learning, data science, AI engineering, or related field.
• Hands-on experience building, tuning, validating, and deploying ML models.
• Strong knowledge of supervised/unsupervised learning, regression, classification, forecasting, NLP, model evaluation.
• Proficiency in Python and SQL.
• Experience with ML libraries: pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Practical understanding of MLOps: model registry, experiment tracking, CI/CD, deployment pipelines, monitoring, drift detection, retraining.
• Experience with enterprise data platforms, cloud platforms, and modern data engineering practices.
• Strong data quality, feature engineering, model validation, and production support skills.
• Ability to translate business needs into effective AI/ML solution designs.
• Clear communication skills for technical and non-technical audiences.

Preferred Skills

• Experience in healthcare, dental insurance, health insurance, financial services, or regulated environment.
• Exposure to Azure ML, Dataiku, Databricks, Snowflake, MLflow, GitHub, GitHub Actions, Power BI, or similar.
• Experience supporting GenAI/LLM solutions in an enterprise setting.
• Familiarity with AI solutions on enterprise data platforms (Snowflake, cloud data ecosystems).
• Experience with responsible AI, model governance, bias detection, explainability, audit requirements.
• Background supporting AI governance councils, architecture reviews, or model risk review processes.
• Knowledge of healthcare data domains: members, providers, claims, benefits, eligibility, call center, clinical, dental, operational data.
• Collaborative, curious, solutions-oriented approach with passion for reliable AI outcomes.