Are you prepared to join a groundbreaking initiative aimed at revolutionizing traffic systems through state-of-the-art technology?Experience:
- At least 3 years of hands-on experience in MLOps, specializing in computer vision applications, with expertise in ML model development, deployment, and monitoring.
- Proficiency in Python is essential.
- Practical experience with ML pipeline tools like Kubeflow, MLflow, TFX, and Airflow is required.
- Familiarity with popular libraries such as Pytorch, OpenCV, and TensorFlow is expected.
- Proficient in managing large-scale datasets for computer vision tasks.
- Proven ability to develop auto-annotation tools for visual data.
Nice to Have:
- Previous experience collaborating with both internal and external data annotation services is a must.
- Knowledge of active learning, semi-supervised learning, and similar approaches for visual data analysis.
- Demonstrated expertise in MLOps best practices and automation.
- 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.
- 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.