Apr 2023 – Feb 2024 · 10 mos · Remote

• Facilitated infrastructure consolidation into a single cloud provider for better overall monitoring and lower
• money spend (up to 30% cost reduction);
• Developed infrastructure and CLI-tool (to remove overhead of learning K8s or AWS) for Data Scientists to
• easily scale training to multiple nodes;
• Singlehandedly importing existing and defining new infrastructure (AWS) from the ground up in Terraform
• and Terragrunt;
• Cost monitoring and optimization;
• Implementation of CI/CD for easy and quick deployment to staging and production environments;

Jun 2022 – Feb 2024 · 1 yr 8 mos · Remote

• Various projects in the fields of CV, NLP and MLOps. Example projects per different clients:
• Set up dev and prod ECS services for deployment of ML services based on Streamlit;
• Set up CI/CD pipeline through Github Actions and CodeBuild to automatically update ECS services;
• Managing cloud-based infrastructure (AWS) through Terraform and Terragrunt
• Developed object detection and tracking system for vehicle counting;
• Quantized to int8;
• Deployed on cameras and Nvidia Jetson, close to 100% accuracy;
• Consolidated infrastructure in a single place (AWS);
• Built and improved existing CI/CD pipelines for deployment of NLP models with Github Actions;
• Fully automated training pipeline with further improvements;
• Set up frontend deployment through AWS Beanstalk and Cloudfront;
• Set up data upload page for client data upload directly to S3 with Amplify and Cognito;
• Productionized several generative models including StableDiffusion, Anycost GAN, StyleGAN and more, deployed through ECS and Celery, interaction with frontend through Pusher;
• Set up AWS and GCP for simultaneous use by Data Scientists;
• Developed CLI-tool that handled that multicloud setup internally, allowing Data Scientists only specify required resources without worrying about cloud-related intricacies.
• Technologies/tools:
• Python, FastAPI, Flask, Tensorflow, TensorRT, PyTorch, Docker, Docker Compose, AWS, GCP

Sep 2021 – Apr 2022 · 7 mos

Jan 2019 – Apr 2022 · 3 yrs 3 mos

• Responsibilities:
• Lead multiple teams of total up to 15 CV/ML engineers and Python developers from intern to senior level to implement new features by running design and code meetings, producing status reporting to management and resolving resourcing and design/code issues.
• Mentor and perform code reviews for team members.
• Interact on a daily basis with clients inside and outside the company.
• Main projects include image classification, object detection and tracking, pose estimation, facial recognition.
• Technologies/tools:
• Python, OpenCV, Numpy, Flask, Tensorflow, Keras, FFmpeg, Docker, Kubernetes

Mar 2021 – May 2022 · 1 yr 2 mos · Helsinki, Southern Finland, Finland

Jun 2020 – Mar 2021 · 9 mos · Helsinki, Southern Finland, Finland

Apr 2018 – Dec 2018 · 8 mos · Moscow, Russian Federation

• Responsibilities:
• Video analysis, boundary shot detection.
• Video enhancements: super resolution, SDR to HDR conversion.
• Interaction with other teams inside the company.
• Administration of GPU servers.
• Technologies/tools:
• Python, OpenCV, Numpy, Flask, Tensorflow, Keras, Tensorlayer, FFmpeg

Jul 2017 – Apr 2018 · 9 mos · Moscow, Russian Federation

• Responsibilities:
• Further development of existing recommender system;
• Exploration and research of possible models for recommender systems, mainly neural networks;
• Infrastructure improvements;
• Lecturing in ML/DL internal courses.
• Technologies/tools:
• Python, Numpy, Pandas, Flask, Redis, Hadoop, Spark, Hive, Scikit-learn, Tensorflow

Nov 2016 – Oct 2019 · 2 yrs 11 mos · Amherst, MA, USA

• Responsibilities:
• Analysis of NGS data from various organisms;
• Mentoring and guidance in bioinformatics for 4 students both in English and Russian.

Jun 2016 – Dec 2016 · 6 mos · Москва, Россия

• Responsibilities:
• WGBS/RRBS data analysis of humans and rats derived samples.

Nov 2015 – Jun 2017 · 1 yr 7 mos · Москва, Россия

• Responsibilities:
• Development and maintenance of analysis pipelines, that are being developed in collaboration with medical geneticists and oncologists;
• Analysis of NGS data, including variant calling and annotation;
• Development of quality assessment tools using machine learning approaches;
• Administration of Gitlab server.

Feb 2013 – Jul 2014 · 1 yr 5 mos

Feb 2013 – Jul 2014 · 1 yr 5 mos