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Experience level:
3-5 years
Education level:
Languages:
Russian: Native
English: A2
Serbian: B1
QA Automation Engineer with 2.5+ years of experience building test frameworks in Python.
Specialize in end-to-end API automation (Pytest) and UI automation (Playwright), integrating tests into CI/CD pipelines, and using Docker to create stable test environments. I write code that breaks other code — so that in the end nothing breaks.
Stack: Python / Bash / Linux (Ubuntu) / Networking (DHCP, DNS, TCP/IP) / Jira / System Administration
Deployment and automation of test infrastructure: Managed the Linux-based test lab. Wrote Python and Bash scripts that automated routine tasks for QA engineers, such as log collection and test bed configuration.
Result: My scripts reduced manual operation time by ~40%. This allowed the QA team to work faster and more efficiently, which helped me find and thoroughly document over 50 bugs in Jira.
Stack:
Python / Pytest / Playwright / API & UI Automation / E2E Testing / Jira / Confluence / Test Design
Development of API autotests for a FinTech platform: Designed from scratch and implemented a Pytest framework for testing key business logic and data integrity.
Result: Achieved ~90% coverage of critical endpoints, which minimized the risk of financial errors and increased system stability.
Implementation of UI automation to accelerate releases: Automated a full regression E2E test suite on Playwright for a new web product.
Result: Reduced the regression testing cycle from 5 days to 4 hours, allowing the dev team to release new features 50% faster.
Strengthening the QA team before release: As an external expert, automated the most labor-intensive manual test cases to offload the internal team.
Result: My work freed up ~20 hours per week for staff QA engineers, letting them focus on exploratory testing of new functionality and improving release quality.
Standardization of QA and AQA processes: Developed and implemented a unified system of test documentation (test plans, checklists) and bug tracking in Jira and Confluence.
Result: Increased transparency of the testing process and reduced communication time between QA and development, ensuring more predictable release timelines.
Stack:
Python (asyncio, aiogram) / OpenAI API / Hugging Face / PyTorch / SQLAlchemy / Django (DRF) / Git / Docker
LLM integration for business process automation: Designed and implemented a solution based on OpenAI API for data analysis and classification. Conducted iterative prompt testing and prepared a proof-of-concept for fine-tuning the model on internal company data.
Result: Reduced task processing time by ~30%.
CI/CD implementation and test automation: Implemented automated API testing using Pytest and configured a CI/CD pipeline in GitHub Actions for automatic code validation.
Result: Endpoint test coverage ~65%, reducing regression bugs by 87%.
Payment gateway architecture (ERC-20): Designed and implemented REST API on Django (DRF), optimizing architecture to handle over 100 transactions daily.
Result: Deployed stable data pipeline with 99.9% uptime, delivering over 3000 clean and structured events (transactions) per month, ready for use in ML models.
Async Telegram bot for AI services: Implemented an asynchronous Telegram bot (aiogram, asyncio) designed as a scalable inference client.
Result: Architecture supports over 50 concurrent model requests per day and reduced response time by 25%, ensuring infrastructure readiness for real-time ML model integration and operation.
Game modules with token integration: Developed modules with cryptocurrency transactions (TRX network) for the “Battleship” game in Telegram, generating user action events.
Result: Increased user engagement by 15%, creating a valuable dataset suitable for training AI-based recommendation systems.
User experience improvement: Wrote Python scripts for parsing and processing unstructured data from web resources, creating an ETL process for integration into the main system.
Result: User experience improved by 18%. Built a pipeline that delivers ready data for potential NLP tasks.
Backend: Python; FastAPI; Django ORM (+ DRF); Flask; SQLAlchemy
ML/AI: Hugging Face; PyTorch; OpenAI API (fine-tuning); MLOps (DVC, MLflow); Cursor; xAI
Database: PostgreSQL; Redis; SQL; MongoDB
Frontend: HTML5/CSS; JavaScript
Tools & DevOps: Docker (+ docker-compose); Kubernetes (Orchestration); Git (+ CI/CD); Linux
API & Testing: HTTP API; REST API; Unit tests; Postman; pyTest