• Develop and implement NLP models and algorithms for text processing and language understanding. • Work on text classification, sentiment analysis, named entity recognition (NER), and text summarization. • Perform text preprocessing, tokenization, stemming, lemmatization, and feature extraction. • Build and evaluate machine learning and deep learning models for NLP applications. • Work with NLP libraries such as NLTK, spaCy, Hugging Face Transformers, and Gensim. • Develop solutions using Large Language Models (LLMs), where applicable. • Work with transformer architectures such as BERT and other relevant language models. • Collect, clean, analyze, and prepare textual datasets for model training. • Evaluate model performance and improve accuracy, efficiency, and scalability. • Collaborate with data scientists, ML engineers, and software developers to integrate NLP models into applications. • Deploy and maintain NLP solutions using APIs and cloud platforms. • Document workflows, experiments, and model performance. Requirements • Strong programming knowledge of Python. • Understanding of Natural Language Processing (NLP) concepts. • Knowledge of machine learning and deep learning fundamentals. • Familiarity with NLP libraries such as NLTK, spaCy, or Hugging Face Transformers. • Understanding of text preprocessing, tokenization, and vectorization techniques. • Knowledge of neural networks and transformer-based models. • Familiarity with NumPy, Pandas, and Scikit-learn. • Basic understanding of SQL and data handling. • Knowledge of model evaluation techniques and performance metrics. • Strong analytical, problem-solving, and communication skills.