Ілля

Ilya Gilev
Technical Support Engineer L2\L3
📧 [відкрити контакти](див. вище в блоці «контактна інформація»)
🔗 [відкрити контакти](див. вище в блоці «контактна інформація»)
🐙 github.com/GilevIlya
📧 t.me/Gilev1lya

Technical Support Engineer with strong backend development roots and production experience building and deploying Web APIs. Adept at deep technical troubleshooting, debugging infrastructure issues, and optimizing database queries to resolve complex incidents. Proficient in Python, PostgreSQL, Docker, and Nginx. I bridge the gap between customer-facing issues and core engineering teams by analyzing system logs, diagnosing root causes in code, and resolving L2/L3 tickets independently.
Technical Skills

Backend
Python 3 · FastAPI · Django · Asyncio · Aiohttp · Aiogram (bots) · httpx · Pydantic · JWT auth
Databases
PostgreSQL · SQLite · Redis · SQLAlchemy ORM · Alembic (migrations) · raw SQL
Testing
Pytest · unit tests · integration tests · test design
DevOps
Docker · Docker Compose · Nginx · Git · Grafana · Linux · Uv · SSL certificate setup
Cloud
AWS S3 — file upload, retrieval, and access control
Arch.
Onion Architecture · OOP · design patterns · clean code principles
Also
Django (self-study) · REST API design · Postman · third-party API integration

Project Experience

Backend Developer
Linguo — Spaced Repetition Learning Platform
2025-11 – 2026.07
Remote
FastAPI · PostgreSQL · SQLAlchemy · Alembic · AWS S3 · Pytest · Docker · Nginx · Asyncio
What Linguo does:
A platform that helps users learn vocabulary, terms, and definitions through spaced repetition — an algorithm that schedules review sessions based on memory strength.
Backend responsibilities:
• Designed and implemented service layer following Onion Architecture — clear separation between domain logic, application services, and infrastructure, making each layer independently testable
• Built full CRUD REST API endpoints with FastAPI, including request validation via Pydantic, error handling, and response serialization
• Integrated AWS S3 for all file operations: uploads, signed URL generation, retrieval, and deletion — with proper access control
• Connected to multiple third-party external APIs, handling auth flows, retries, and error normalization
• Wrote unit and integration tests with Pytest, covering both happy paths and edge cases
• Participated in database schema design; managed all migrations with Alembic
• Handled deployment to development and production environments: server provisioning, Docker setup, Nginx configuration, and SSL certificate installation. Monitored application health and system metrics using Grafana to proactively identify bottlenecks.
• Contributed to Agile/Scrum ceremonies: sprint planning, backlog refinement, daily standups, and code reviews
Currently Building

Actively expanding backend depth through deliberate practice:
• Deepening understanding of PostgreSQL performance — query planning, indexing strategies, EXPLAIN ANALYZE
• Studying system design fundamentals: caching patterns, async task queues, API versioning
• Learning AI integration patterns — prompt engineering and LLM API integration for backend systems

On Using AI in Development

I use AI as a support tool — not a replacement for understanding. In daily work I use AI coding agents (Cline, Antigravity CLI) alongside LLMs (Gemini, DeepSeek, StepFun, Poolside) for research, debugging, and boilerplate, while owning the architecture and reviewing every output myself. My view: a tech support engineer needs to master algorithms, data structures, design patterns, and best practices through manual practice first. Only then can you use AI deliberately and critically, reviewing its output with the judgment to know when it's right, wrong, or subtly problematic. I'm building that foundation now.
Languages

Ukrainian Native
English B2 Upper-Intermediate — reading technical docs, written async communication, team collaboration