The AI/ML Engineer is a hands-on technical expert responsible for developing, deploying, and monitoring machine learning models and AI-based tools within a secure analytical environment. This role focuses on applying advanced machine learning techniques, natural language processing (NLP), and causal AI to solve complex healthcare data problems, ensuring model explainability, compliance, and optimal performance. Working closely with Data Science Leads, data engineers, and evaluation specialists, the AI/ML Engineer contributes to building reproducible and impactful models that enhance analytical capabilities.
Responsibilities:
- Develop and implement robust machine learning pipelines utilizing various supervised and unsupervised learning models, including gradient boosting, logistic regression, and deep learning architectures
- Implement causal inference algorithms to estimate treatment effects and understand drivers of outcomes in observational healthcare data
- Apply Natural Language Processing (NLP) techniques to extract valuable insights from unstructured text data, such as provider documentation and survey feedback
- Deploy and integrate machine learning models into cloud-based analytical infrastructure (e.g., Databricks, Snowflake) ensuring scalability and reliability
- Ensure the reproducibility and version control of models and analytical workflows using tools like MLflow and maintaining comprehensive audit logs
- Integrate explainability tools (e.g., SHAP, LIME) to support model transparency, interpretability, and ethical considerations
- Conduct rigorous validation tests and collaborate closely with quality assurance teams to ensure model robustness, accuracy, and adherence to performance benchmarks
- Collaborate with evaluation leads and data scientists to integrate machine learning insights seamlessly into comprehensive reports and analytical products
- Document modeling assumptions, performance metrics, feature engineering processes, and hyperparameter tuning parameters for clarity and auditability
- Continuously research and stay current with emerging AI/ML techniques, particularly those applicable to healthcare analytics and data privacy
Experience Required:
- 5+ years of practical experience in AI/Machine Learning engineering, preferably in healthcare, public sector, or other data-intensive domains
- Proven ability to develop, deploy, and monitor machine learning models in a production environment
- Hands-on experience with cloud-based data and machine learning platforms (e.g., Databricks, Snowflake, AWS ML services)
Certifications / Education:
- Proficiency in Python (including libraries like Scikit-learn, XGBoost, TensorFlow, PyTorch) and/or R (required)
- Master’s degree (MS) in Data Science, Computer Science, Artificial Intelligence, or an equivalent quantitative field
Skills:
- Deep Learning, NLP
- Causal AI, Model Deployment
- Reproducibility Frameworks (e.g., MLflow)
- Python, R
- TensorFlow, Scikit-learn, XGBoost, PyTorch
- Databricks, Snowflake
- Statistical Modeling
- Data Preprocessing
- Problem-Solving
- Collaboration
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