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.

• 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

• 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.