Machine Learning Ops Engineer 2
Modernizing MedicineCandidates 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.
- Salary not specified
- Full Time
- Junior (1 to 2 years)