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.

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.