NLP Engineer

• 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.