Position: Machine Learning Engineer [CV]
Category: Data Science
English level: upper
Years of experience: 3

1. Product data processing pipeline creation. Detectors, segmentation models integration, postprocessing. Interesting CV algorithms for image processing. 
2. Team leading, project owning without much experience.
**Tech Lead**
- Developed process pipeline for the solution. Integration and unit test writing. CI/CD GitHub actions.
- Computer vision algorithms development and implementation. NN-models integration and post-processing. 
- Dockerisation for pipeline parts. 
- Organised department's successful work in the organisational structure change. Project owning on every step. Business/Engineering balance.
- Product integration.
- **Buzzwords**: Neural networks, Testing, Integration Tests, Python, Computer Vision, OpenCV, Docker, AWS, 3d basics.
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**Software Engineer**
- Neural networks in the browser. Tensorflow.js. Valila javascript.
- Model converting and serving. Building the validation pipeline based on the model output. Metric gathering.
- Computer vision application for Apparel Industry.
- **Buzzwords**: Neural networks on the Edge, Testing, Integration Tests, Python, JavaScript, Tensorflow.js, Computer Vision, OpenCV.
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**Team Lead**
- Building the engineering group from scratch. The organisation of advertisement, interviewing, onboarding, contracts, instructions, equipment, etc. 
- Mentoring, knowledge building. Algorithms, best practices, etc.
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**Software Engineer**. Computer vision system for mechanical systems automatisation.
- R&D with industrial cameras for automation. 
- NN for defect detection in real-time. Keras and TensorFlow. YOLOv4, YOLOv4 tiny.
- Built Opencv with GPU so It can accelerate the inference.
- Dataset creation. Annotation, cleaning. 
- **Buzzwords**: Computer Vision [CV], OpenCV, Neural Networks[NN, DNN], Python, Data gathering and preparation, LabelImage, Data processing, Data augmentation, Keras, Tensorflow, YOLOv4.
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**Software Engineer**. **Additional contract**. Product POC.
- Landmark detector training. YOLOv4 tiny, config tuning, anchors reconfiguration.
- Aggregating training dataset from existing and annotation of new data. Unification of the annotation. 
- Landmark detector demo for clients' usage. Video feed processing.
- **Buzzwords**: Computer Vision [CV], OpenCV, Neural Networks[NN, DNN], Python, Data gathering and preparation, LabelImage, Data processing, Data augmentation, Keras, Tensorflow, YOLOv4.
Adequate salary, stable workflow and processes.
Strong team. R&D will be a plus.