Python Developer with ML

Luxoft Poland
  • Post Date: October 6, 2024
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Job Overview

Project Description:We’re seeking a dedicated MLOps/Dataset Engineer to join our team. This role involves iterative dataset improvements, auto-annotation, large-scale data scanning, and constructing robust infrastructure around our machine learning models, with a particular focus on computer vision and related tasks.
Responsibilities:- Design and develop large-scale data scanners and auto-annotation engines for optimizing computer vision datasets- Collaborate closely with internal and external data annotation teams and services to enhance dataset production- Create, deploy, and automate ML pipelines, ensuring an efficient transition from model training to deployment- Monitor model performance and manage models and datasets versioning to bolster operational efficiency- Participate in end-to-end development, from problem statement and data aggregation to model design, experiments, deployment, and iterative improvement automation
Mandatory Skills Description:- BS, MS, or PhD in Machine Learning, Computer Science, Electrical Engineering, or a related field- Minimum 3 years of experience in MLOps, with a focus on computer vision applications, including building, deploying, and monitoring ML models- Proficiency in Python- Proven experience with ML pipeline tools and services, such as Kubeflow, MLflow, TFX, Airflow, Vertex AI, Dataflow, DVC etc.- Familiarity with standard tools and libraries, e.g. Pytorch, OpenCV, Tensorflow, CVAT, Albumentations, Cleanlab etc.- Hands-on experience with large-scale data and datasets management for computer vision tasks- Demonstrated expertise in developing robust auto-annotation tools for visual data- Experience in integrating with and utilizing external and internal data annotation services
Nice-to-Have Skills Description:- Knowledge of active learning, semi-supervised learning, and other similar approaches for visual data analysis- Demonstrated experience in automating machine learning pipelines and understanding of MLOps best practices- General understanding of DL models development and their deployment process on both embedded platforms such as Nvidia Jetson and various cloud inference solutions- Experience with multi-task model training and semi-supervised DL model training on video data- Proven track record – significant industry experience and/or publications at venues such as ICRA, RSS, IROS, or CVPR

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