- 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, spa Cy, 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

- Robust 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, spa Cy, or Hugging Face Transformers.

- Understanding of text preprocessing, tokenization, and vectorization techniques.

- Knowledge of neural networks and transformer-based models.

- Familiarity with Num Py, 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.

📌 NLP Engineer (India)
🏢 Training Basket
📍 India