DevOps Engineer (MLOps)
Loop- Full Time
- Junior (1 to 2 years)
Candidates should possess experience with deploying models in production using cloud platforms like AWS Sagemaker, GCP AI Platform, or Azure ML Studio, along with experience utilizing MLOps tools such as MLflow, Kubeflow, or Airflow to automate and monitor the lifecycle of machine learning models. Furthermore, candidates are expected to have experience setting up scalable and fault-tolerant infrastructure to support model training and inference in cloud environments such as AWS, GCP, or Azure.
The Machine Learning Ops Engineer 2 will develop, deploy, and manage ML models on Databricks using MLflow for tracking experiments, managing models, and registering them in a centralized repository, collaborate with the data engineering team to integrate model pipelines with real-time and batch data processing frameworks, implement monitoring systems to track model performance, accuracy, and drift over time, and create automated systems for re-training and continuous learning to maintain optimal performance.
Specialty-specific electronic health record systems
Modernizing Medicine provides specialty-specific Electronic Health Records (EHR) systems designed to improve the workflow of healthcare providers. Their main products, EMA and gGastro EHR, help users manage patient information and administrative tasks more efficiently, allowing them to concentrate on patient care. These systems adapt to the specific practices of each user, enhancing their effectiveness. Unlike many competitors, Modernizing Medicine focuses on tailored solutions for various medical specialties, which sets them apart in the healthcare technology market. The company's goal is to streamline healthcare delivery and improve patient outcomes by providing tools that simplify administrative processes.