Artem

Artem Zaporozhchenko
Python Backend & AI Engineer
Mykolaiv, Ukraine | [відкрити контакти](див. вище в блоці «контактна інформація») | [відкрити контакти](див. вище в блоці «контактна інформація») | Telegram: @aaz_dev

SUMMARY
Backend Engineer with 3 years of commercial experience specializing in Python, FastAPI, asyncio, and modern AI and
LLM integrations. Focused on designing clean, maintainable architectures for async microservices, RAG and agentic
workflows with LangGraph and pgvector, and resilient background task processing with Celery, RabbitMQ, and Redis.
Pragmatic engineer who values fast shipping and system reliability, experienced in optimizing PostgreSQL performance,
structuring cloud storage workflows with AWS S3, and automating delivery through CI/CD pipelines.

SKILLS
- Languages & Core: Python 3.11+, FastAPI, asyncio, Pydantic v2, WebSockets, Celery, HTTPX / aiohttp,
Aiogram 3.x, Playwright, BeautifulSoup4
- Databases & Cache: PostgreSQL, MySQL, Redis, RabbitMQ, MongoDB, pgvector, Qdrant, SQLAlchemy 2.0,
Alembic
- AI & Search: LangGraph, LangChain, RAG architectures, OpenAI & Anthropic API, Structured Outputs / Tool
Calling, Local Embeddings, Whisper, Deepgram
- DevOps & Tooling: Docker, Docker Compose, GitHub Actions, CI/CD, AWS S3, Linux/Bash, uv, Poetry, Git,
Sentry, Loguru
- Testing & Security: Pytest, pytest-asyncio, Unit & Integration tests, Mypy, Ruff, JWT, Argon2, RBAC

EXPERIENCE
Python Backend & AI Engineer
Tech Consulting & Development | Oct 2024 – Present
- Developed async FastAPI microservices handling real-time WebSocket communication and 1k+ concurrent client
connections with sub-100ms API response times.
- Built RAG and multi-agent workflows using LangGraph and LangChain, integrating OpenAI and Anthropic APIs
with pgvector HNSW semantic search, improving retrieval relevance by ~25%.
- Integrated AWS S3-compatible storage using Presigned URLs for direct client uploads, eliminating server-side I/O
bottlenecks.
- Offloaded CPU-bound and media-processing tasks to background workers via Celery using RabbitMQ and Redis,
keeping the event loop non-blocking under high-throughput workloads.
- Optimized SQL queries and table schemas in PostgreSQL using EXPLAIN ANALYZE, adding composite/partial
indexes and reducing p95 query latency by ~35%.
- Set up GitHub Actions CI/CD pipelines for automated code quality checks with Ruff and Mypy, and test execution
with pytest-asyncio.
- Integrated Sentry and structured Loguru logging for real-time exception tracking, correlating error details with
database entities.
- Created multi-stage Docker builds containerized with Docker Compose, reducing final image footprint by ~40%
and improving deployment speed.

Python Backend Developer
Digital Product Agency | Jul 2023 – Sep 2024
- Built production-ready RESTful APIs using FastAPI and asynchronous SQLAlchemy 2.0, structuring data
migrations and schema versioning via Alembic.
- Implemented secure JWT-based authentication using Argon2 password hashing and role-based access control
across modular API endpoints.
- Architected WebSocket-based session management backed by Redis Pub/Sub for low-latency multi-user room
events and real-time state synchronization.
- Developed resilient asynchronous web scrapers with HTTPX, Playwright, and BeautifulSoup4, processing 50k+
daily target pages with automated proxy rotation and retry logic.
- Standardized Docker and Docker Compose workflows across development and staging environments, integrating
structured JSON logging via Loguru for centralized log analysis.
- Enforced code quality standards by introducing Ruff and Mypy alongside automated test suites using Pytest and
pytest-asyncio to prevent regressions.