Senior Machine Learning Engineer

Job Description

We are looking for an experienced Machine Learning Engineer to lead the development and deployment of ML models that power intelligent products and insights. You will collaborate with teams across Data Science, Engineering, and Product to build solutions that are scalable, efficient, and impactful. Candidates with exposure to the healthcare domain are encouraged to apply, although this is not a mandatory requirement . Key Responsibilities: Architect, build, and maintain end-to-end ML systems from data pipelines to model deployment. Develop and optimize machine learning models for use cases such as classification, prediction, recommendation, NLP, or computer vision. Implement MLOps best practices for model training, tracking, deployment, and monitoring. Collaborate with data scientists and domain experts to productionize prototypes and research. Evaluate and monitor model performance; ensure robustness, fairness, and explainability. Document architecture and processes; contribute to knowledge sharing and code reviews. (Preferred) Work with EHR data, claims data, clinical notes, or healthcare interoperability formats like HL7 or FHIR, if applicable. Required Skills & Qualifications: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related discipline. 5+ years of hands-on experience in ML engineering or applied data science. Strong command of Python and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost). Experience deploying ML models in production using containerization (Docker, Kubernetes) and cloud platforms (AWS/GCP/Azure). Familiarity with MLOps tools like MLflow, DVC, or Kubeflow. Proficient in building ETL/ELT pipelines and handling large-scale datasets. Strong understanding of statistical methods, model evaluation metrics, and optimization techniques. Good software engineering practices (version control, testing, CI/CD). Preferred Qualifications: Exposure to healthcare datasets such as medical claims, EHR/EMR, HL7, FHIR, or medical coding (CPT, ICD-10). Experience with NLP models applied to clinical documentation or unstructured medical data. Understanding of HIPAA compliance, data anonymization, and PHI handling. Contributions to open-source ML projects or peer-reviewed publications. Experience working in regulated industries or mission-critical environments.