• Undergraduate/Master's student status at the time of employment
• Knowledge of statistics and ML/DL algorithms
• Knowledge of Python and the ML stack (PyTorch, Scikit-learn, Pandas)
• Understanding of modern NLP (tokenization, data processing, embeddings, pre-train models, etc.)
• Experience in designing and training neural network models (from CNN/RNN to transformers)
• Experience in building RAG (classical RAG, GraphRAG, Agentic RAG)
• Knowledge of classical algorithms (two pointers, binary search) and data structures (heap, stack, binary tree)
• Proficient English for reading and analyzing scientific articles and documentation

Conditions:

• Competitive salary
• Voluntary medical insurance and corporate training
• Career growth

Skills:

• Python
• PyTorch
• Scikit-learn
• Pandas
• NLP
• RAG
• GraphRAG
• Agentic RAG
• CNN
• RNN
• Transformers