MLOps Engineer, Machine Learning Engineer (Healthcare)

TL;DR

MLOps Engineer, Machine Learning Engineer (Healthcare): Building and scaling machine learning solutions for the healthcare revenue cycle with an accent on operationalizing ML models, deployment pipelines, and scalable, secure ML infrastructure on AWS. Focus on automation, monitoring, continuous improvement, and ensuring compliance with healthcare data standards.

Location: Remote (United States)

Salary: $133,109 – $239,596

Company

hirify.global is a global data and technology company focused on powering opportunities across various markets, including financial services and healthcare.

What you will do

• Develop scalable MLOps pipelines for model training, validation, deployment, and monitoring using AWS.
• Implement infrastructure as code and CI/CD workflows for rapid experimentation and reliable production releases.
• Collaborate with data scientists to productionize ML models, ensuring reproducibility, versioning, and traceability.
• Monitor model performance and data drift in production environments, and implement automated retraining and alerting mechanisms.
• Improve ML workflows using tools such as SageMaker, Airflow, Docker, Kubernetes (EKS), and Step Functions.
• Ensure compliance with healthcare data standards and security best practices (e.g., HIPAA).

Requirements

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.
• 3+ years' experience in MLOps, DevOps, or ML engineering roles.
• 3+ years' experience with AWS services for ML (e.g., SageMaker, Lambda, S3, ECR, CloudWatch).
• Proficiency with containerization and orchestration tools (Docker, Kubernetes/EKS).
• Experience with ML lifecycle tools such as MLflow, TensorFlow Serving, or Kubeflow and with CI/CD pipelines, infrastructure as code (e.g., Terraform, CloudFormation), and monitoring/logging tools.
• Experience in the healthcare domain, especially with claims or EHR data, and familiarity with standards like ICD and CPT.
• English: B2 required.

Nice to have

• Exposure to NLP, Bayesian modeling, or real-time ML systems.
• Familiarity with Agile development methodologies.
• AWS certifications (e.g., Machine Learning Specialty, DevOps Engineer).

Culture & Benefits

• Great compensation package and bonus plan.
• Core benefits including medical, dental, vision, and matching 401K.
• Flexible work environment and flexible time off including volunteer time off.
• Commitment to an inclusive environment, work/life balance, and professional development.
• Globally recognized as a World's Best Workplaces by Fortune and Great Place To Work.