Expert AI/ML Engineer

Required Qualifications 8+ years of experience in machine learning, data science, AI engineering, ML engineering, or related roles. Strong hands-on experience building, tuning, validating, and deploying ML models. Experience mentoring data scientists, ML engineers, data engineers, or analytics teams. Strong knowledge of supervised learning, unsupervised learning, classification, regression, forecasting, NLP, and model evaluation techniques. Experience with Python and common ML/data science libraries such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, PyTorch, or similar. Practical experience with 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 understanding of data quality, feature engineering, model validation, and production support. Ability to translate business problems into AI/ML solution designs. Strong communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders. Technical Skills Programming: Python, SQL Machine Learning: scikit-learn, XGBoost, TensorFlow, PyTorch, statistical modeling, forecasting, NLP MLOps: MLflow, Azure ML, Dataiku, model registry, CI/CD, GitHub Actions Data Platforms: Snowflake, Azure SQL, Oracle, data lakes, cloud data platforms AI/GenAI: LLMs, prompt engineering, RAG, semantic search, text-to-SQL, document intelligence Governance: model documentation, lineage, metadata, data quality, responsible AI, privacy and security controls Desired Skills: Preferred Qualifications Experience in healthcare, dental insurance, health insurance, financial services, or another regulated industry. Experience with platforms such as Azure ML, Dataiku, Databricks, Snowflake, MLflow, GitHub, GitHub Actions, Power BI, or similar tools. Experience with GenAI and LLM-based solutions. Experience designing AI solutions using enterprise data platforms such as Snowflake or cloud-based data ecosystems. Experience with responsible AI, model governance, bias detection, explainability, and audit requirements. Experience supporting AI governance councils, architecture reviews, or model risk review processes. Experience with healthcare data domains such as members, providers, claims, benefits, eligibility, call center, clinical, dental, or operational data.