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.Qualifications:
Nice to Have:
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.
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.