Back
Mid-level ML role, metro location, generalist MLOps/cloud skills and broad requirements increase candidate competition.
Core ML/MLOps skills transfer across industries, but healthcare compliance requirements raise domain sensitivity to medium.
Explicit 3–5 years requirement, 2 years in ML production, and specific MLOps/cloud/HIPAA skills increase filter strictness.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Build and maintain CI/CD pipelines and deployment environments for machine learning models focused on healthcare information systems.
Develop and optimize data engineering pipelines to transform healthcare data formats (FHIR, HL7) for ML training and inference.
Ensure production reliability through monitoring, containerization (Docker, Kubernetes), security compliance (HIPAA, HITRUST), and integration of ML outputs into healthcare applications.
Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or related field.
3–5 years of professional experience in software or data engineering with at least 2 years in machine learning production environments.
Proficiency in Python and SQL; experience with cloud providers (AWS, Azure, or GCP) and containerization (Docker).
Mandatory knowledge of MLOps tools (e.g., Airflow, Prefect, BentoML, Kubeflow) and ML libraries (PyTorch or Scikit-learn).
Experienced in bridging data science and software engineering with strong expertise in MLOps and production ML systems in regulated environments like healthcare.
Skilled in designing scalable APIs and microservices for ML model serving with a strong focus on system performance and stability.
Familiar with feature store management, data pipeline optimization, and compliance with healthcare data security standards (HIPAA, HITRUST).