Key Responsibilities :• Legal documents analysis;• Building RAG system to automate lawyers tasks: - Text Processing : baai embedders | sentence-transformers | nltk | pysbd | unstructured; - Text Generation : Mistral & LLaMa along with T5 based models; - Vector DBs : Qdrant, FAISS - Advanced : hybrid search + reranking + custom text chunking strategies - Research : runpod + vast• Teamwork • Consulting Projects: RAG-based system for lawyers workflows automation.

Key Responsibilities : • Java Back-End Development: SpringBoot; • GPT - based LLMs fine-tuning using Low Rank Adaptation(LoRA); • Code generation using GPT-based models; • Prompt Engineering and Inference Experiments; • Local Deployment using Flask and text-generation-inference; • Teamwork & Communication with Java Department;Projects: DDD-like Java Code generation : Finally, we optimised java routine and accelerated the development process.

Key Responsibilities : • Orthodontics Investigation: It was a new research area for me, but nevertheless and despite the difficulties I was able to understand my colleagues from the medical department, in terms of project, and work with patient care chats. • Chats Analysis to improve the quality of treatment service: - Text Analysis : LDA | TF-IDF | yake | KeyBERT | gensim | spacy; - Text Processing : sentence-transformers | nltk | TF-IDF | (t-SNE, UMAP, PCA); - Embeddings Cluster Analysis : K-Means | DBSCAN | HDBSCAN; - Automatic Data Annotation; • LLaMa-based LLMs fine-tuning using Low Rank Adaptation(LoRA); • Text-Classification using LLaMa based-LLMs; • Teamwork & Communication with Medical Department: I had a possibility to work with extremely professional machine learning engineers and learned lot's of new things, it was a great experience.