Никита
Python-програміст
- Age:
- 25 years
- City of residence:
- Odesa
- Ready to work:
- Remote
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PYTHON ENGINEER
CONTACT PROFILE
[
alongside hands-on experience with PostgreSQL, including writing DDL and
[
implementing complex algorithms using tools such as NumPy, pandas, SciPy,
Odesa, Ukraine and deap. Additionally, I have basic knowledge of Angular and JavaScript,
complementing my backend development skills. I am eager to combine my
[
kita-mikhalchenko-677b3b240/
WORK EXPERIENCE
EDUCATION
SignalHire July 2025 - March 2026
2018-2023 Junior Python engineer
NATIONAL UNIVERSITY
Developed an embedding service based on ChromaDB for storing and
"ODESA POLYTECHNIC"
managing vectorized data, including version control for different
Bachelor at Applied Math dataset variations;
Built an MCP-based project to demonstrate how MCP works and how
to create an LLM chat system integrated with external tools and
SKILLS services;
Developed a service for company name search and renaming using
LLMs, including prompt design, API implementation, and database
Python (FastAPI, SQLAlchemy)
integration.
HTML\CSS Built a RESTful API service for storing and managing deep learning
JavaScript tasks, including user management, LLM model configuration, and
dataset upload support.
Angular
PostgreSQL NDA (startup\freelance) August 2024 - June 2025
Pandas, Celery, Pytest Intern Python engineer
NumPy, SciPy, deap, unsloth Developed a modular framework for streamlined plugin development,
enabling easy integration and extensibility for new functionalities.
Docker Built scraping tools and API integrations for extracting structured data
LangGraph from diverse external sources.
Applied prompt engineering techniques to optimize LLM outputs for
domain-specific tasks.
LANGUAGES Designed and implemented optimization algorithms to solve complex
logistics and routing problems.
Russian (Fluent) Developed machine learning and statistical models for accurate time-
German (Basics) series forecasting and predictive analytics.
English (Upper-Intermediate) Implemented a Retrieval-Augmented Generation (RAG) system to
enhance the contextual reasoning capabilities of LLM-based agents.
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