Machine Learning Engineer for NLP Project
Skills Required
Description
Natural Language Processing projects demand an engineer who can combine theoretical knowledge with practical implementation skills. The focus is on developing models that understand and process human language effectively.
Python will serve as the primary programming language, leveraging libraries like Pandas for preprocessing large volumes of text data. Cleaning, tokenizing, and normalizing text are critical steps before model training.
Deep learning frameworks such as TensorFlow and Keras will be used to build and train models. From RNNs and LSTMs to Transformers, the developer should be comfortable experimenting with different architectures.
Performance metrics like accuracy, precision, recall, and F1-score must guide the evaluation of models. Without proper validation, the results may fail to generalize beyond the training data.
A strong understanding of embeddings is essential. Using techniques such as Word2Vec, GloVe, or contextual embeddings allows models to capture semantic relationships between words.
Feature engineering still plays an important role in NLP. Even with deep learning, decisions about what features to include can significantly impact model performance.
Data augmentation techniques may be applied to handle smaller datasets. This could include paraphrasing, synonym replacement, or back-translation to increase data diversity.
Hyperparameter tuning is another responsibility. Adjusting learning ra...