Position: Machine/Deep Learning Engineer
Category: Python
English level: upper
Years of experience: 7

• Top 67% in Kaggle HPA - Single Cell Classification competition
• Top 67% in Kaggle Shopee - Price Match Guarantee competition
• 5x Magento Certified
• Rich software toolset and keen interest in science
I have been doing commercial Software Engineering for more than 7 years. This is how I spent that time:

• 2+ years in Python and Machine Learning / Deep Learning Engineering
• 5+ years in eCommerce Magento Software Development (from which 3+ years in technical management)

The very last project I have been working on is a code execution engine for Data Scientists for one of the big Boston-based AI platforms (employing more than 1200+ people). Namely:

  Designed, implemented and owned the deployment of the whole system to AWS based on Kubernetes, Helm Charts and ArgoCD and Flux as a gitOps CD
 Designed a custom Kubernetes operator based on the RedHat OperatorFramework (GoLang)
 Designed and implemented a websocket microservice based on Socket.IO and Typescript/Node.JS
 Implemented the central orchestration part of the project (a Python-based microservice)
 Worked and extended the Jenkins-based CI pipelines
 Led integration of the system with the observability stack based on Elastic Stack (aka ELK stack) in terms of logging, metrics collection and distributed tracing
 Led the security review of the system and identified the main points of k8s security improvements
 Took active part in architectural discussions and improvements, code reviews in a geographically distributed team (Ukraine and East US time zones) of more than 20 people. Effectively handled cross-team communication with other DataRobot teams to resolve roadblocks and meet project expectations and deadlines

Before that, I had worked on:

 Designed and implemented an internal framework for building ML-enabled applications using by modern Python stack (Flask, Pydantic, FastAPI)
 Using the framework, I have taken part in the implementation of a complaint review application for a US company from the healthcare sector. Took part in complaint analysis and NLP modeling pipeline using huggingface transformers stack.
 Took part in configuring and troubleshooting Kubernetes-based environments deployed on Gitlab instances

Besides that:

 I managed and led several software development cross-functional teams of different sizes (from 6 to 15 people)
 Took a direct part in identifying and framing client’s problems and designing solutions to solve them
 Actively blogging about various technical topics I have been exposed to
I'm currently available to join and enhance a great R&D team of professionals that are actively involved into solving problems via Machine Learning, Deep Learning, research and innovations.