MLOps Engineer

About:

We're at the forefront of an exciting project, leveraging technology to bring groundbreaking solutions to life by harnessing the power of technology, Transforming the traffic system

Requirements:

• Education: BS, MS, or PhD in Machine Learning, Computer Science, Electrical Engineering, or a related field.
• Experience: Minimum 3 years in MLOps, specializing in computer vision applications, including ML model development, deployment, and monitoring.
• Skills: Proficiency in Python. Hands-on experience with ML pipeline tools such as Kubeflow, MLflow, TFX, Airflow, and more. Familiarity with common libraries like Pytorch, OpenCV, Tensorflow, and others.
• Data Handling: Proficiency in managing large-scale datasets for computer vision tasks. Demonstrated expertise in developing auto-annotation tools for visual data.
• Collaboration: Experience in working with external and internal data annotation services.

Nice to Have:

• Knowledge of active learning, semi-supervised learning, and similar approaches for visual data analysis.
• Demonstrated experience 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 data scanners and auto-annotation engines for optimizing computer vision datasets.
• Collaborate with data annotation teams to enhance dataset production.
• Create, deploy, and automate ML pipelines for efficient model training and deployment.
• Monitor model performance and manage model and dataset versioning for operational efficiency.
• Participate in end-to-end development, from defining problems to model design, deployment, and continuous improvement.