Job Description

MLOps is a set of management techniques for the deep learning or production ML lifecycle, formed from machine learning or ML and operations or Ops. These include ML and DevOps methods, as well as data engineering processes for deploying and maintaining machine learning models in production.

Design and implement cloud solutions, build MLOps on cloud (AWS, Azure, or GCP)
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
Communicate with a team of data scientists, data engineers and architect, document the processes

Requirements

Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS, MS Azure or GCP)
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.
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

Nice to have