A Tenth Revolution Group Company

Ref: 21023JS

Machine Learning Operations Engineer (MLOps)

Poland, Lesser Poland

Job description

Machine Learning Operations Engineer (MLOps)


Are you ready to be part of a groundbreaking project that's set to revolutionize traffic systems using cutting-edge technology? Seeking a talented individual to help lead an exciting endeavor.


  • Education: A minimum of a Bachelor's degree (BS), Master's degree (MS), or Ph.D. in Machine Learning, Computer Science, Electrical Engineering, or a closely related field.

  • Experience: At least 3 years of hands-on experience in MLOps, with a specialization in computer vision applications. This includes expertise in ML model development, deployment, and monitoring.

  • Skills: Proficiency in Python is a must. You should also have practical experience with ML pipeline tools like Kubeflow, MLflow, TFX, and Airflow. Familiarity with popular libraries such as Pytorch, OpenCV, and TensorFlow is expected.

  • Data Handling: Proficient in managing large-scale datasets for computer vision tasks and a proven ability to develop auto-annotation tools for visual data.

  • Collaboration: Previous experience collaborating with both internal and external data annotation services.

Nice to Have:

  • Knowledge of active learning, semi-supervised learning, and similar approaches for visual data analysis.

  • Demonstrated expertise in MLOps best practices and automation.

  • A general understanding of DL model development and deployment on embedded platforms and cloud solutions.

  • Experience with multi-task and semi-supervised DL model training on video data.

  • A proven track record in industry experience or publications in relevant conferences.

  • Proficiency in additional languages is a plus.


  • Design and develop cutting-edge data scanners and auto-annotation engines to optimize computer vision datasets.

  • Collaborate closely with data annotation teams to enhance the production of high-quality datasets.

  • Take the lead in creating, deploying, and automating ML pipelines for efficient model training and deployment.

  • Continuously monitor model performance and effectively manage model and dataset versioning to ensure operational efficiency.

  • Participate in the entire development lifecycle, from problem definition to model design, deployment, and ongoing improvement.