TL;DR

ML Engineer (MLOps): Building and maintaining AI pipelines in Healthcare Information Systems with an accent on MLOps, data reliability, and production stability. Focus on implementing automated workflows, managing cloud infrastructure, and ensuring secure and scalable AI services.

Location: Remote (United States). Must be legally authorized to work in the United States without sponsorship for employment visa status (e.g., H1B status). May include up to 10% domestic travel. New employees hired for this position with a start date of October 1st 2025 or later will be required to travel to a designated company location for on-site onboarding during their initial days of employment.

Company

%hirify_global% is a new healthcare company focused on solving big challenges that improve lives and help healthcare professionals perform at their best by pioneering game-changing innovations at the intersection of health, material, and data science.

What you will do

Build and maintain CI/CD pipelines for machine learning, focusing on automated testing, model deployment, and version control.
Deploy and monitor ML models as scalable APIs and microservices, ensuring performance, latency, 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 Python code, use Docker and Kubernetes for orchestration, and ensure compliance with 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 focused on machine learning production environments.
Strong proficiency in Python and familiarity with SQL; hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) and Docker.
Familiarity with ML libraries (PyTorch or Scikit-learn) and MLOps tools (e.g., Airflow, Prefect, BentoML, or Kubeflow).
Experience with data processing frameworks (e.g., Pandas, Spark, or dbt).
Must be legally authorized to work in the United States without sponsorship for employment visa status (e.g., H1B status).

Nice to have

Familiarity with deploying Large Language Models (LLMs) or using frameworks like LangChain.
Experience working in a regulated environment (Healthcare, Finance, etc.).
Understanding of API design and microservices architecture.

Culture & Benefits

Offers many programs to help employees live their best life, both physically and financially, with competitive pay and benefits.
Committed to maintaining the highest standards of integrity and professionalism.
Provides consideration for employment without discrimination based on race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.