Responsibilities and results:1) Creation of pre-sales materials with a focus on the technological aspects of the solution,2) Doing technical calls with the Clients with AI/ML tools suggestions,3) Creation of internal documentation about the use of AI/ML tools in the development process,4) Consulting and knowledge sharing sessions conducting,5) Conducting technical engineering interviews with the approved candidates ,6) Testing third-parties AI/ML solutions before the production stage,7) Hiring people to the team,8) Built a recommendation engine and classifier model for the Client, Azure based solution,9) Made system design documents for a Client and for an internal AI/ML project,10) Fine-tuned and updated Microsoft PHI-3 LLM on custom datasets with the use of Autotrain library on Hugginface

Responsibilities and results: 1) Developed a pipeline based on insightface library for Face Recognition task2) Created a full pipeline of 3d head generation from one single image 3) Creating a database of Deep Fake videos using various deep learning techniques from downloaded and splitted Youtube videos 4) Research of applications, light-weighted neural networks and frameworks for age/race/gender detection on ARM processors5) Researching and compiling a demo of an application for gaze detection6) Created a dataset for the European traffic signs detection task, which has been used for training detection and classifier models

Responsibilities and results:1) Making a Docker image to run Core library inside within the Openvino dependencies requirements 2) Preparing validation datasets for Detection and Tracking algorithms in MOT format 3) Preparing training datasets from customer’s data for custom Detection and Classification use cases, and training and validating models on them4) Models conversion, optimization and delivery5) Research on a more accurate Tracking algorithms with checking them on customers’ videos 6) Core code refactoring

Responsibilities and results:1) Team Leading of a team of 4 developers - delivering tasks2) Conducting technical interviews on Computer Vision Engineer and Backend Engineer positions3) Code refactoring 4) Code optimization5) Checking and testing new approaches for facial parsing algorithms 6) Making data acquisition process faster

Responsibilities and results:1) Conducting technical interviews on Data Scientist, Machine Learning Engineer, Computer Vision Engineer positions2) Collecting data for Object Detection and Tracking tasks for running at Jetson Nano device3) Making Knowledge Transfer sessions with short explanation of modern approaches related to business problems 4) Providing presales materials 5) Team leading a project related to faster rendering6) Making a lot of research about suitable approaches and datasets

Responsibilities and results:1) Implemented a sonar images generation pipeline with GANs - the use of synthetic dataset improved quality of object detection for 14%2) Did research on underwater dehazing pipelines3) Trained a multi-class segmentation baseline for ice ground segmentation4) Worked on video superresolution baseline research5) Explore and rum demos on baseline networks for 3d multi-view reconstruction6) Did research about pipelines on object detection for ARM processors

Responsibilities and results:1) Worked on data pipelines 2) Research on 3d rendering and Gans staff

Responsibilities and results:1) Research and enhancement of current pipeline for face swap - working on making high resolution of face swap2) Research of face detection and landmarks detection frameworks concerning their inference time on different servers

Responsibilities and results:1) Game optimization models provision

Responsibilities and results:1) Developing a classification model on several classes inside

Responsibilities and results:1) Trained several GAN models for response text generation in the Russian and English languages2) Worked on optimization of inference time - current model in production works 2.5 times faster than the previous one3) Made a model of paragraph sentence ratio with 80% AUC qualit

Responsibilities and results:1) Training GANs for making synthetic datasets for face identification (standard models don’t work because of camera distortion)2) Training face identification model on prepared data and implementing it to current framework3) Training Object Detection model on satellite imagery Improved quality of face identification model to 25%.

Responsibilities and results:1) Did Domain Adaptation with GANs to provide data of various weather conditions for training models. It has helped to improve a quality of Detection and Instance Segmentation models up to 30% in night and rain weather conditions 2) Prepared data for training Classifier model of 200 classes (overmapped existed classes and got statistics of them)3) Trained models for Instance Segmentation task 4) Made internal tools to Data Science team for better obtaining data for training models5) Kaggling a lot at free time (Data Science Bowl -Top14%, Camera Identification Challenge - Top39%, models for WAD CVPR Challenge (segmentation)