Mikhail Zotochev

Angestellt, Software Developer, Wargaming
Belgrade, Serbien

Fähigkeiten und Kenntnisse

Python
Backend Development
pytest
PostgreSQL
Game Programming
Cython
Automated Machine Learning (AutoML)
Docker Products
Kubernetes
FastAPI
OpenAPIs
C++
C (programming language)
docker
CatBoost
SQLAlchemy
Sanic
WebSocket
Microservices
Swift (Programming Language)
Redis
Scikit-Learn
Nginx
Linux
Software Development

Werdegang

Berufserfahrung von Mikhail Zotochev

  • Bis heute 1 Jahr und 11 Monate, seit Sep. 2023

    Software Developer

    Wargaming

    • Built a custom game unit: navigation, targeting, damage & self-destruction. • Used Python, C++, Cython to develop high-performance gameplay features. • Applied TDD (unittest, PyTest) for stable, testable iterations. • Engineered XML-configurable asymmetric matchmaking for special modes. • Designed gameplay events: AI, abilities, rewards, time-limited challenges. • Optimized matchmaking via batching & priority queues (10K+ players). • Integrated new battle types and modernized legacy C++ systems.

  • 1 Jahr und 6 Monate, Apr. 2022 - Sep. 2023

    Backend Developer

    Digital Cube

    Projects: Telecom CRM, Hotel System, Framework Extension • Split monolith into microservices with Docker & K8s → better scalability. • Built code generator with logging, routing, and monitoring (30× faster setup). • Designed hotel backend, integrated Oracle PMS via binary Unix socket. • Created YAML VoIP UI, SOAP SIM service with retries & monitoring. • Automated OpenAPI docs & live-data PDF contracts. • Built WebSocket + Redis Kanban board with drag-and-drop sync.

  • 5 Monate, Aug. 2021 - Dez. 2021

    Backend Developer

    SandLabs

    • Worked on backend development for a company website, implementing several API endpoints using Sanic and SQLAlchemy. • Built a chatbot for retrieving cryptocurrency rates using aiogram and Python, which was deployed on Linux.

  • 7 Monate, Jan. 2021 - Juli 2021

    ML Developer

    Mindset

    • Contributed to developing a low-code AutoML library by extending core functionality for model training and reporting automation. • Designed and implemented a unified method for extracting model coefficients across multiple model classes (e.g., CatBoost, scikit-learn).

Ausbildung von Mikhail Zotochev

  • 1 Jahr und 3 Monate, Okt. 2020 - Dez. 2021

    Software Engineering

    École 42

  • 4 Jahre und 10 Monate, Sep. 2007 - Juni 2012

    Construction Engineering

    Ulyanovsk State Technical University

Sprachen

  • Englisch

    Fließend

  • Deutsch

    Grundlagen

  • Russisch

    Muttersprache

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