Artem Kuznetsov

Domain: Lifescience Responsibilities: ● Implementing Python plugins, APIs for molecular design tool ● Refactoring current plugins ● Increased test coverage for existing application (from 20 to 80) ● Created common plugin structure with cookiecutter tools ● Created simple script in Node.js Tools and Technologies: Python, FastAPI, Pydentic, Pytests, Pandas, terraform, cookiecutter, Node.js, GitLab, Jenkins AWS tools: S3, Secrets Manager, CloudWatch, ECS Fargate

Domain: Software & Hi-Tech Responsibilities: ● Developed new REST API services using Flask with external API integrations, following the Service Layer pattern ● Implemented inter-service communication functionality using AWS Cognito tools ● Analyzed existing and third-party services for future integration and backlog planning ● Wrote documentation and testing guidelines ● Profiled endpoint performance using PyInstrument, cProfile, snakeviz, and memory-profiler Tools and Technologies: Python, Flask, Redshift/PostgreSQL, Pytests, boto3, Pandas, terraform, PyInstrument, cProfiler, snakeviz, memory-profiler AWS tools: Secrets Manager, CloudWatch, Cognito, ECS

Domain: Business Information & Media Responsibilities: ● Developed ETL pipelines using AWS S3 – Airflow – OpenSearch stack ● Migrated pipeline stages from Python 2 to Python 3.11, applying modern design principles such as DRY, SOLID, and design patterns like Template Method and Strategy ● Identified performance degradation in a partitioned table (~500GB, 20 partitions) caused by an incorrect partition key using Explain Analyze and Airflow logs ● Implemented a thread synchronization mechanism using the Event() object to temporarily reduce load on the OpenSearch cluster Tools and Technologies: Python, Apache Airflow, PostgreSQL, Pytest, Docker, XML, Explain Analyze. AWS tools: S3, CloudWatch, ElastiCache (Redis)

Domain: Business Information & Media Responsibilities: ● Integrated internal Django/Django REST portal with Jira and ServiceNow APIs ● Created and updated Celery tasks for processing reports stored in an S3 bucket ● Covered new functionality and previously untested code with unit tests (unittest) to improve service stability ● Added a new S3 Ninja service to the Docker Compose setup for local S3 bucket interaction Tools and Technologies: Python, Django, Django Rest Framework, PostgreSQL, pytest-django, Celery, Docker, Docker, Jira API, ServiceNow API

● Created new and improved existing CRUD functionality for a Django + Django REST monolith ● Optimized Celery tasks and Flower monitoring (queue prioritization, identifying and fixing blocking tasks) ● Debugged asynchronous microservices built with FastAPI (Telegram, Email, Widget chats) forming an event-driven architecture (WebSockets, RabbitMQ, Redis) ● Made minor changes to a Kotlin-based microservice ● Developed REST API for a loyalty program using FastAPI ● Implemented CRUD operations for integration with a mobile app, payment gateway (SOAP), external APIs, and messaging platforms ● Created periodic Celery tasks for sending email notifications, distributing promotional campaigns to users, and synchronizing user data and bonus transactions ● Worked with PostgreSQL database: wrote SQL queries using SQLAlchemy ORM and managed migrations with Alembic ● Wrote functional tests using pytest

● Create, update AWS lambdas ● Write unit tests for lambdas ● Optimize SQLAlchemy queries (load options) ● Close work with data engineers to connect materialized views with python models ● Creation REST Api with authorization ● Integrate GraphQL ● Investigation of Keycloak as an identity and access management tool.

● Investigate and integrate an open-source ML library (Skills- ML) in the project ● Update the Skills-ML library to ensure compatibility with latest version of Python (Python 3.7 and up) ● Expand the existing API by integrating Google Calendar API ● Optimize SQLAlchemy queries ● Create, update, and delete endpoints for API ● Implement notifications by integrating a socket server using the Flask-Socket.IO library ● Processing and converting large files in multiple streams

My own projects and technologies: Flask: - Creation microblog and deploying on Heroku - Creation service for handling Docker containers Django: - Creation microblog with PostgreSQL - Creation Restfull API with Django Rest Framework and containerizing it by Docker. Others: - Testing RestApi - Writing a console chat - Creation VagrantBox based on Ubuntu - scrapping website

Deep learning in Python Language, Data structure, testing, OOP, Database, Iterators, Web Final project - Flask service for handling Docker-containers.