Expert AI/ML Engineer

Responsibilities

• Design, build, train, validate, and deploy machine learning models for a range of business and healthcare analytics use cases.
• 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.
• 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.
• Aid 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.