Join a high-impact enterprise AI and machine learning team supporting healthcare analytics and operations through the development of practical, production-ready ML solutions. This hybrid role offers the opportunity to build, tune, validate, and operationalize models while helping shape MLOps practices, governance standards, and responsible AI approaches from concept through production.

Responsibilities

• Design, build, train, validate, and deploy machine learning models for a range of 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 to improve model results.
• Evaluate model outputs and recommend enhancements to accuracy, stability, fairness, explainability, and overall reliability.
• Troubleshoot issues related to data quality, model drift, inconsistent outputs, and production performance.
• Partner with data engineering, analytics, and business teams to ensure models are built on trusted, governed data.
• Mentor team members and provide hands-on 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 and machine learning delivery.
• Contribute to MLOps practices, including experiment tracking, versioning, automated testing, CI/CD, deployment, and monitoring.
• Support model promotion workflows, retraining strategies, rollback planning, alerting, and production support processes.
• Assist in identifying and shaping AI use cases across reporting, operations, governance, and automation.
• Provide technical guidance for GenAI and LLM-based solutions such as RAG, semantic search, text-to-SQL, summarization, and document processing.
• Support governance and compliance activities, including documentation, validation, risk review, auditability, and responsible AI practices.

Skills

• 8+ years of experience in machine learning, data science, AI engineering, ML engineering, or a related field.
• Hands-on experience building, tuning, validating, and deploying machine learning models.
• Strong knowledge of supervised and unsupervised learning, regression, classification, forecasting, NLP, and model evaluation.
• Proficiency in Python and SQL.
• Experience with common ML and data science tools and libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Practical understanding of MLOps concepts such as model registry, experiment tracking, CI/CD, deployment pipelines, monitoring, drift detection, and retraining.
• Experience working 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 and machine learning solution designs.
• Clear communication skills with the ability to explain technical concepts to both technical and non-technical audiences.

Preferred Skills

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

Horizontal Talent is committed to fostering an inclusive workplace where diverse perspectives are valued, and every team member is supported and respected. We welcome applicants from all backgrounds and encourage individuals with a wide range of experiences to apply.