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:

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  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.
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  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.
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  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.
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  Data Handling: Proficient in managing large-scale datasets for computer vision tasks and a proven ability to develop auto-annotation tools for visual data.
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  Collaboration: Previous experience collaborating with both internal and external data annotation services.

Nice to Have:

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  Knowledge of active learning, semi-supervised learning, and similar approaches for visual data analysis.
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  Demonstrated expertise in MLOps best practices and automation.
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  A general understanding of DL model development and deployment on embedded platforms and cloud solutions.
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  Experience with multi-task and semi-supervised DL model training on video data.
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  A proven track record in industry experience or publications in relevant conferences.
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  Proficiency in additional languages is a plus.

Responsibilities:

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  Design and develop cutting-edge data scanners and auto-annotation engines to optimize computer vision datasets.
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  Collaborate closely with data annotation teams to enhance the production of high-quality datasets.
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  Take the lead in creating, deploying, and automating ML pipelines for efficient model training and deployment.
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  Continuously monitor model performance and effectively manage model and dataset versioning to ensure operational efficiency.
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  Participate in the entire development lifecycle, from problem definition to model design, deployment, and ongoing improvement.