Building and maintaining the pipelines that power AI in Healthcare Information Systems (HIS)
Ensuring models work reliably in the real world
Bridging the gap between data science and software engineering by implementing automated workflows, managing cloud infrastructure, and ensuring AI services are secure and scalable
Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control (using tools like MLflow or Git)
Deploy ML models as scalable APIs and microservices, ensuring they meet performance and latency requirements for clinical use
Implement basic monitoring tools to track model performance, data drift, and system health in production
Develop and optimize ETL processes to transform healthcare data (FHIR, HL7) into clean, usable datasets for model training and inference
Help build and maintain feature stores and data layers that ensure consistency between training and production environments
Work closely with backend teams to integrate ML outputs into core healthcare applications
Write clean, maintainable, and well-documented Python code
Participate in code reviews to ensure system reliability
Use Docker and Kubernetes to package and orchestrate ML workloads across different environments
Follow established protocols to ensure all data handling and deployments meet HIPAA and HITRUST security standards

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related field
3–5 years of professional experience in software engineering or data engineering, with at least 2 years focused on machine learning production environments
Strong proficiency in Python and familiarity with SQL
Knowledge of a compiled language (like Go or Java) is a plus
Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and containerization (Docker)
Familiarity with ML libraries (PyTorch or Scikit-learn) and MLOps tools (like Airflow, Prefect, BentoML, or Kubeflow)
Experience with data processing frameworks (like Pandas, Spark, or dbt)
Familiarity with deploying Large Language Models (LLMs) or using frameworks like LangChain is a plus
Experience working in a regulated environment (Healthcare, Finance, etc.) is a plus
Understanding of API design and microservices architecture

ML Engineer

Key skills