Position: MLOps/Dataset Engineer - work only from Poland
Company: Luxoft
Experience required: 3y
English level: upper

Project Description:
We are utilizing a city's existing fleet and a growing number of mobile devices connected to our network, we collect real-time data that supports the enforcement of traffic laws. We are implementing the first technology provider to create a vision-based solution capable of detecting, understanding, and determine causation of traffic violations, to help cities achieve their Vision Zero safety goals.
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:
- 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
- Polish language