Shokh (Mufazzalshokh) Ulashev
Hi, I’m a backend and full-stack developer with a focus on building scalable API-driven platforms.
About
Hi, I’m a backend and full-stack developer with a focus on building scalable API-driven platforms. I design secure architectures using Python, Django, FastAPI, and PostgreSQL, and I enjoy turning complex requirements into clean, maintainable code. I have hands-on experience with Docker, Nginx, and AWS-ready deployments, and I’ve led QA/testing efforts in Scrum environments to ensure reliable releases. I’m excited about integrating AI solutions like Retrieval-Augmented Generation, chatbots, and prompt engineering into real-world products. I value clean code, observability, and thorough testing, and I love collaborating across teams to deliver impactful software.
- 5 years' experience
- Joined August 2025
Skills· 25
Languages
- LatvianIntermediate
- UzbekFluent
- EnglishFluent
- RussianAdvanced
Industry experience
- Government
- Software & Internet
- Real Estate & Construction
- Education
- Telecommunications
- Professional Services
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Work experience· 5 roles
Architected and delivered a full-stack e-commerce platform integrated with an OpenAI GPT-based chatbot for smart customer assistance. Implemented secure payment flow, dynamic product management, user behavior tracking, and session-based cart logic. Applied prompt engineering and error-handling logic to deliver consistent chatbot responses across multiple intents. Deployed the full system locally using Docker and prepared CI-ready architecture for production deployment. Independently built a scalable property listing engine with advanced filtering, geo-tagged search, and user dashboards. Engineered a robust web scraping solution in Python to extract property data from multiple portals, normalizing and storing in a structured PostgreSQL schema. Designed REST APIs and managed deployment using Docker, Nginx, and Gunicorn on Ubuntu for high-availability access. Consulted clients on feature planning, user journey mapping, and SEO-friendly backend structure.
Built a mini Retrieval-Augmented Generation (RAG) system with FastAPI for question-answering over local documents. Implemented hybrid retrieval, Server-Sent Events (SSE) streaming, and budget guardrails for controlled LLM usage. Integrated OpenAI API, prompt engineering, and citation mechanisms for trustworthy answers. Added retry logic, exponential backoff, and in-memory rate limiting to handle API and user constraints. Focused on production-quality design with clean architecture, observability, and test coverage.
Built a mini Retrieval-Augmented Generation system with FastAPI for Q&A over local documents. Implemented hybrid retrieval, Server-Sent Events streaming, and budget guardrails for controlled LLM usage. Integrated OpenAI API, prompting engineering, and citation mechanism for trustworthy answers. Added retry logic, exponential backoff, and in-memory rate limiting to handle API and user constraints. Focused on production-quality design with clean architecture, observability, and test coverage. Included automated testing and CI/CD integration for deployment to cloud environments.
Education· 6
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