About me
Currently, being a Technical Lead @Modelplace.AI startup, develop a service that provides Plug & Play artificial intelligence solutions to democratize the development of AI-based applications.
If you want to get more information about my work experience and education, please see the information below.
Work experience
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2020 - present - Technical Lead @Modelplace.AI
Being a technical lead of the development team, from scratch built an infrastructure and developed a service that provides Plug & Play artificial intelligence solutions to democratize the development of AI-based applications.
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2018 — 2020 - Deep Learning Engineer @Xperience.AI
Worked on Computer Vision 3D scene reconstruction projects which were mentioned on Apple WWDC20.
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2017 – 2018 - Data Scientist @Neuron.digital
Created NLP models (sentiment analysis, document classification, entity extraction), scraped data from different web sources, modelled tasks to label the collected data and developed web interfaces for real-time execution and visualization.
Skills
Hard
- Programming language: Python 3
- Data Science: Classic ML and CV & NLP DL (PyTorch) experience
- DevOps: Docker, Kubernetes, Terraform, Ansible
- Backend: FastAPI
- Others: Linux, Git, CI/CD, Networking
Soft
- Task planning, teamwork organization and building the development processes
- People (teams up 9 developers) and project management at the intersection of the product and the development teams
- External companies and customers communication
- Participation in webinars as a speaker
Personal
- Well-organized, self-demanding, flexible and structural type of thinking developer who decomposes tasks, meets the deadlines, makes clear progress and who can work with a lot of uncertainty
- Experienced in developing product-based services (and educated based on GoPractice Simulator)
- Experienced in interviewing and growing IT specialists
Education
- 2018 – 2020 - Master in Data Mining @ Higher School of Economics
- 2014 – 2018 - Bachelor in Applied Mathematics and Information Science @ Higher School of Economics
Honours and awards
- Paper: Sentiment Analysis Using Deep Learning, Computational Aspects and Applications in Large Scale Networks, NET 2017, Nizhny Novgorod, Russia, June 2017, Kalyagin, V.A., Pardalos, P.M., Prokopyev, O., Utkina, I. (Eds.)
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Competition: Sbertech / Sberbank Hackathon
- Award: 1st place
- Task: To predict how a user rates Sberbank Online app based on a comment
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Conference: Intellectual city — a scientist at the crossroads of science
- Award: 1st place
- Section: Computer Science
- Topic: Applying machine learning methods to the texts in social networks
- Key components: The overview of state-of-the-art machine learning methods in sentiment analysis tasks