NLP Engineer

Responsibilities

• 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

• Programming: Strong programming knowledge of Python.
• NLP concepts: Understanding of Natural Language Processing (NLP) concepts.
• Machine learning: Knowledge of machine learning and deep learning fundamentals.
• Libraries: Familiarity with NLP libraries such as NLTK, spaCy, or Hugging Face Transformers.
• Text processing: Understanding of text preprocessing, tokenization, and vectorization techniques.
• Neural networks: Knowledge of neural networks and transformer-based models.
• Data tools: Familiarity with NumPy, Pandas, and Scikit-learn.
• Databases: Basic understanding of SQL and data handling.
• Model evaluation: Knowledge of model evaluation techniques and performance metrics.
• Soft skills: Strong analytical, problem-solving, and communication skills.