MLOps

About:

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.

Skills:

• 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.

Data Handling:

• Proficient in managing large-scale datasets for computer vision tasks.
• Proven ability to develop auto-annotation tools for visual data.

Collaboration:

• Previous experience collaborating with both internal and external data annotation services is a must.

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.
• 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.

Responsibilities:

• 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.