About the Role
We are seeking an experienced Senior Machine Learning Engineer for a remote contract engagement to design, deploy, and scale machine learning models within our healthcare ecosystem. In this role, you will bridge the gap between healthcare data science prototypes and robust production environments. You will play a pivotal role in building secure, compliant pipelines that directly impact clinical decision support and operational efficiency.

Key Responsibilities
Production Deployment: Build, deploy, and monitor scalable ML models and production-grade inference pipelines within cloud environments (AWS/Azure).
Healthcare Data Pipeline Engineering: Design and maintain feature stores and data workflows handling diverse, complex medical datasets, specifically Electronic Health Records (EHR) and clinical imaging.
MLOps & Lifecycle Management: Implement robust MLOps practices using tools like MLflow, managing model versions, tracking data drift, and ensuring high-availability monitoring.
Compliance & Security: Ensure all model architectures and infrastructure strictly adhere to health data privacy regulations, including HIPAA compliance.
Collaboration: Partner with clinical data scientists and software developers to translate research code into scalable healthcare applications.

Required Skills & Qualifications
Experience: 5+ years of dedicated professional experience as an ML or MLOps Engineer.
Healthcare Domain Expertise: Direct experience working with EHR data, clinical imaging, and ensuring strict HIPAA compliance.
Cloud Platforms: Strong hands-on proficiency with AWS (SageMaker) or Microsoft Azure (Azure ML).
MLOps Tools: Practical experience with MLOps and tracking tools, specifically MLflow.
Core Languages & Frameworks: Expert proficiency in Python and SQL, alongside frameworks like PyTorch, TensorFlow, or Scikit-Learn.

Senior Machine Learning Engineer

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