Principal Machine Learning Engineer - Medical Guardian

Key Responsibilities

• Architect, develop, and productionize scalable machine learning pipelines for real‑time health monitoring and emergency response.
• Collaborate with data engineers, clinicians, and product teams to define problem statements, data requirements, and model evaluation criteria.
• Lead model experimentation using deep learning frameworks (TensorFlow, PyTorch) and optimize performance for low‑latency inference on cloud infrastructure.
• Mentor senior engineers and establish best practices for model versioning, testing, and continuous integration/continuous deployment (CI/CD) using Docker and Kubernetes.
• Monitor deployed models in production, conduct root‑cause analysis of drift, and implement automated retraining workflows on AWS.

Requirements

• 10+ years of professional experience in machine learning, with a proven track record of delivering production‑grade models in health‑tech or related domains.
• Expertise in Python and deep learning libraries such as TensorFlow or PyTorch, including model architecture design and hyperparameter tuning.
• Strong background in cloud platforms (AWS) and containerization technologies (Docker, Kubernetes) for scalable deployment.
• Experience with data pipelines, feature engineering, and handling large, multimodal health datasets.
• Excellent communication skills and ability to lead cross‑functional teams in a fast‑moving, regulated environment.