Natural Language Processing (NLP) Engineer (REMOTE)

Job Summary:

The Natural Language Processing (NLP) Engineer will develop and implement advanced language models to enhance the organization's capabilities in understanding, processing, and generating human language. This role requires a strong background in NLP techniques and machine learning, with the ability to build, fine-tune, and deploy models to production. The ideal candidate will work closely with cross-functional teams, including data scientists and software engineers, to deliver state-of-the-art NLP solutions that drive innovation and improve user experience.

Key Responsibilities:

• NLP Model Development: Design, build, and implement NLP models and algorithms for tasks such as text classification, sentiment analysis, named entity recognition, language translation, and question-answering systems.
• Text Preprocessing: Develop pipelines for data cleaning, tokenization, lemmatization, and vectorization to preprocess large volumes of unstructured text data.
• Feature Engineering: Identify and engineer relevant features from text data to improve model performance.
• Model Training & Evaluation: Train, fine-tune, and evaluate NLP models using frameworks like Hugging Face Transformers, TensorFlow, or PyTorch.
• Collaborate with Teams: Work closely with data scientists, software developers, and product teams to integrate NLP models into applications and services.
• Research & Innovation: Stay current with advancements in NLP research, such as large language models (e.g., GPT, BERT), and apply new methods to improve existing products.
• Deploy Models to Production: Deploy and maintain NLP models in production environments, ensuring scalability, performance, and reliability.
• Performance Monitoring: Monitor the performance of deployed models, performing updates and retraining as necessary to ensure continued effectiveness.
• Documentation: Document the development processes, code, models, and outcomes for future reference and reproducibility.

Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Linguistics, or a related field. A Ph.D. is a plus.
  
• Experience:
  • 2+ years of experience in natural language processing, machine learning, or a related field.
  • Hands-on experience with NLP libraries and frameworks such as Hugging Face Transformers, spaCy, NLTK, or Gensim.
  • Experience with machine learning frameworks like TensorFlow or PyTorch.

  • Proficiency in programming languages such as Python or R.

  • Familiarity with deep learning techniques for NLP, such as attention mechanisms and transformer models.
• Skills:
  • Strong understanding of NLP tasks (e.g., text classification, named entity recognition, machine translation).
  • Knowledge of traditional machine learning algorithms (e.g., SVM, Naive Bayes) and modern deep learning models (e.g., RNNs, LSTMs, BERT, GPT).
  • Experience with cloud platforms (AWS, GCP, or Azure) for deploying and scaling NLP models.
  • Strong problem-solving skills and ability to work on complex projects with minimal guidance.
  • Ability to communicate technical concepts clearly to both technical and non-technical stakeholders.
  • Specializes in developing AI systems that can understand and process human language.

  • Expertise in linguistics and programming is essential.

Preferred Qualifications:

• Experience with large-scale language models (e.g., BERT, GPT) and transfer learning in NLP.
• Familiarity with distributed computing and big data technologies like Spark or Hadoop.
• Experience with MLOps practices and tools for deploying and monitoring machine learning models.
• Familiarity with tools such as Docker, Kubernetes, and CI/CD pipelines.
• Knowledge of multilingual NLP models and their application in global contexts.