NLP Engineer

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  Develop and implement NLP models and algorithms for text processing and language understanding.
  
  

  
  


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  Work on text classification, sentiment analysis, named entity recognition (NER), and text summarization.
  
  

  
  


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  Perform text preprocessing, tokenization, stemming, lemmatization, and feature extraction.
  
  

  
  


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  Build and evaluate machine learning and deep learning models for NLP applications.
  
  

  
  


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  Work with NLP libraries such as NLTK, spaCy, Hugging Face Transformers, and Gensim.
  
  

  
  


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  Develop solutions using Large Language Models (LLMs), where applicable.
  
  

  
  


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  Work with transformer architectures such as BERT and other relevant language models.
  
  

  
  


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  Collect, clean, analyze, and prepare textual datasets for model training.
  
  

  
  


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  Evaluate model performance and improve accuracy, efficiency, and scalability.
  
  

  
  


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  Collaborate with data scientists, ML engineers, and software developers to integrate NLP models into applications.
  
  

  
  


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  Deploy and maintain NLP solutions using APIs and cloud platforms.
  
  

  
  


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  Document workflows, experiments, and model performance.
  
  

  
  

Requirements

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  Strong programming knowledge of Python.
  
  

  
  


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  Understanding of Natural Language Processing (NLP) concepts.
  
  

  
  


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  Knowledge of machine learning and deep learning fundamentals.
  
  

  
  


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  Familiarity with NLP libraries such as NLTK, spaCy, or Hugging Face Transformers.
  
  

  
  


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  Understanding of text preprocessing, tokenization, and vectorization techniques.
  
  

  
  


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  Knowledge of neural networks and transformer-based models.
  
  

  
  


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  Familiarity with NumPy, Pandas, and Scikit-learn.
  
  

  
  


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  Basic understanding of SQL and data handling.
  
  

  
  


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  Knowledge of model evaluation techniques and performance metrics.
  
  

  
  


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  Strong analytical, problem-solving, and communication skills.