
THAT WOULD BE WONDERFUL IF YOU HAVE:
- • At least Bachelor’s degree in Computer Science or related field;
• At least 1,5 years of real-world experience in implementing data science and NLP projects;
• Strong Python knowledge;
• Strong knowledge of classical algorithms and data structures;
• Strong knowledge in linear algebra, geometry, calculus, probability theory and statistics;
• Experience with Machine Learning libraries like NumPy, Pandas, ScikitLearn;
• Experience with NLP and Time Series forecasting;
• Experience with generative models (e.g., GPT, Gemini) is a strong advantage;
• Experience with LLMs (e.g., for sentiment analysis, prompt tuning), OpenAI API (incl. fine-tuning and structured output);
• Practical experience with at least one Deep Learning framework like Keras, Tensorflow, PyTorch;
• Basic Git proficiency;
• English — Upper-Intermediate.
WOULD BE A PLUS:
• Experience with LangChain and RAG pipelines;
• Good knowledge and experience with some of the well-known neural networks architectures such as: Yolo, U-Net, MobileNet, ResNet, R-CNN;
• Active Kaggle participation;
• Docker;
• Microsoft Azure or AWS.
ADVANTAGES FOR A CANDIDATE:
• Stable and competitive salary;
• Legally compliant gig contract with official tax reporting and payment;
• Work in a cool & experienced team;
• Useful & exciting projects;
• Up to 26 Days Off per year at your convenience;
• Convenient office in Vinnytsia or an opportunity to work remotely;
• Corporate culture with maximum automation of processes;
• Review of working conditions and position based on performance, productivity and development in accordance with a skills assessment program
• Excellent opportunities and prospects for professional growth in a company with a 9-year history.
ABOUT THE PROJECT
You’ll have the opportunity to join a project focused on building AI-driven analytical tools using generative models. The core of the system is an LLM-based pipeline that processes long-form text, extracts key entities and topics, detects sentiment and bias, and generates structured summaries.





