Responsibilities
- Design and implement cloud solutions, build MLOps on Azure
- Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Circle CI, Airflow or similar tools
- Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality
- Data science models testing, validation and tests automation
- Deployment of code and pipelines across environments
- Model performance metrics
- Service performance metrics
- Communicate with a team of data scientists, data engineers and architect, document the processes
- Candidate Must Have
- Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes, OpenShift
- Programming languages like Python, Go, Ruby or Bash, good understanding of Linux, knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc.
- Knowledge of how and why ML models worktypically the single most important element in MLOps.
- Ability to program, preferably in the languages used by the employer, and script for tasks such as provisioning, configuration and other automation.
- Ability to manage IT infrastructure, including servers, storage, networks and services.
- Ability to deploy and operate complex databases, such as SQL.
- Knowledge of how to store, manage and protect data used to train and run ML platforms.
- Ability to deploy, monitor and manage software, preferably ML models.
- Ability to understand tools used by data scientist and experience with software development and test automation
- Fluent in English, good communication skills and ability to work in a team