- Python programming. 2+ years of experience
- Main fields: Data science, Machine learning, Computer vision, Software engineering, ML engineering
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- Programming languages: Python (Keras, TensorFlow, PyTorch, Scikit-learn, OpenCV, Numpy, Pandas, Matplotlib, Flask), JavaScript (VueJs, ExpressJS)
- Databases: SQL, MongoDB, ElasticSearch
- Web services: REST, RPC
- Team project management: Bitbucket, Git, Jira, Trello
- Operating Systems: Linux OS, Windows OS
- Software Design Methods: Agile
- Version Control Tools: GIT
- IoT: Raspberry Pi
- AWS services: Cloudwatch, EC2, ECR, IAM, Lambda, S3, Sagemaker, SQS
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- BSc IT Engineering, Kaunas University of Technology
- MSc Artificial Intelligence Informatics, Kaunas University of Technology (Last year student)
| Project: Retail Crime Predictor | 2020 02 - 2020 04 |
| Technologies used | Python (OpenCV, Numpy, Pillow, PyTorch, Tensorflow, Scikit-learn), YoloV4, Docker, Convolutional Neural Networks, ElasticSearch |
| Role | ML Engineer, Data analyst |
| Project Description | The main objective - detect if a person is trying to buy an item with permuted barcode. |
| Responsibilities and achievements | Analyze data from stores, provide insights and apply these insights to create ML models. Develop custom classification model training pipelines and automation with SageMaker. Solution pilots are successfully running in multiple stores with plans to expand. |
| Project: Retail Monitoring | 2020 04 - Present |
| Technologies used | Python (MXNet, OpenCV, PyTorch, Tensorflow, Numpy), Convolutional Neural Networks, AWS services (S3), RabbitMQ, Flask |
| Role | Team lead, ML engineer, Data analyst |
| Project Description | The main objective - detect if there are no items on shelves. Detect and classify products identify prices. |
| Responsibilities and achievements | Develop architecture of the project, implement changes. Analyze data and create ML models, logic development. Create ML models to classify each product in a shelf as well as empty spaces, identify prices and assign them to products. Develop processing pipelines to classify data in real-time. Develop main logic to assign each item to a position according to the original planogram. |
| Project: Seat defect identification | 2020 01 - 2020 03 |
| Technologies used | Python (OpenCV, PyTorch, Numpy, MXNet), Convolutional Neural Networks, AWS services (S3, SageMaker), Flask |
| Role | ML engineer, Data analyst |
| Project Description | The main objective - identify car seat defects from images |
| Responsibilities and achievements | Analyze data and create ML models to segment car seat defects. Develop a real-time processing pipeline. Demonstrate solution to the client. |