Natural Language Processing Engineer

Job Summary:

We are seeking a skilled NLP (Natural Language Processing) Engineer to design, develop, and deploy NLP models and applications. The ideal candidate will have strong expertise in linguistics, machine learning, and deep learning techniques to build systems that understand, interpret, and generate human language effectively.

Key Responsibilities:

• Develop and implement NLP algorithms and models for tasks such as text classification, sentiment analysis, named entity recognition, machine translation, summarization, and question answering.
  
• Preprocess and analyze large datasets of text and speech data.
  
• Design, train, and fine-tune deep learning models (e.g., transformers, RNNs, CNNs) using frameworks like TensorFlow or PyTorch.
  
• Collaborate with data scientists, software engineers, and product teams to integrate NLP models into production systems.
  
• Optimize NLP pipelines for performance, scalability, and accuracy.
  
• Stay up-to-date with the latest research and advancements in NLP and AI.
  
• Conduct error analysis and improve models based on feedback and evaluation metrics.
  
• Document methodologies, code, and model architectures.
  

Qualifications:

• Bachelors or Master’s degree in Computer Science, Computational Linguistics, Data Science, or related field.
  
• Strong experience in NLP techniques, machine learning, and deep learning.
  
• Proficiency in Python and NLP libraries such as SpaCy, NLTK, Hugging Face Transformers, Gensim, etc.
  
• Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras.
  
• Familiarity with transformer-based models (BERT, GPT, RoBERTa, etc.).
  
• Solid understanding of linguistics, syntax, semantics, and language modeling.
  
• Experience with cloud platforms (AWS, GCP, Azure) is a plus.
  
• Strong analytical, problem-solving, and communication skills.
  

Preferred:

• Experience with speech recognition, dialogue systems, or chatbot development.
  
• Publications or contributions to NLP research projects.
  
• Knowledge of big data tools and distributed computing.
  
• Familiarity with containerization (Docker, Kubernetes) and DevOps practices