HAYKEL BARGOUGUI

- Led the implementation of advanced audio processing techniques to enhance transcription accuracy under varied conditions. - Assessed and adapted open-source models for improved recognition and transcription of Arabic and English. - Utilized Large Language Models (LLMs) via the GPT API for structured data extraction from transcriptions. Employed advanced techniques like prompt engineering and Retrieval-Augmented Generation (RAG) to optimize API performance and improve results.

- Developed an agent from scratch in LangGraph, employing OpenAI GPT LLMs and tools for action-based search. This development emphasized advanced prompt engineering techniques. - Utilized LangChain and MultiChain to create scalable model chains that enhance the integration and management of external data sources, such as Tavily search. - Experimented with a variety of LLMs (DeBERTa, LLaMA, BERT, T5, GPT, Phi3) and gained expertise in fine-tuning large models using techniques such as LoRA and QLoRA.

- Data collection, data cleaning, data augmentation, and feature selection. - Implemented text vectorization techniques such as TF-IDF, BoW, Word2Vec, Tokenization, Lemmatization, and Stemming. - Developed models including RNN, Random Forest, XGBoost, and Transformer Encoder for sentiment analysis. - Developed and deployed Docker images to Docker Hub, including comprehensive testing and Swagger integration.