Machine Learning Engineer

Role Overview

As an ML Engineer, you will be responsible for building and maintaining the pipelines that power AI in our Healthcare Information Systems (HIS). You will bridge the gap between data science and software engineering by implementing automated workflows, managing cloud infrastructure, and ensuring AI services are secure, scalable, and reliable in production environments.

Responsibilities

• Build and maintain CI/CD pipelines for machine learning using tools like MLflow or Git.
• Deploy ML models as scalable APIs and microservices.
• Implement monitoring tools to track model performance, data drift, and system health.
• Develop and optimize ETL processes for healthcare data (FHIR, HL7).
• Build and maintain feature stores and data layers.
• Write clean, maintainable, and well-documented Python code.
• Use Docker and Kubernetes to package and orchestrate ML workloads.
• Ensure data handling meets HIPAA and HITRUST security standards.

Requirements

• 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 or data engineering, with at least 2 years in machine learning production environments.
• Strong proficiency in Python and familiarity with SQL.
• Hands-on experience with a major cloud provider (AWS, Azure, or GCP).
• Experience with containerization using Docker.
• Familiarity with ML libraries such as PyTorch or Scikit-learn.
• Experience with data processing frameworks like Pandas or Spark.

Skills

• Python
• AWS
• Docker
• PyTorch
• MLflow