What are the responsibilities and job description for the Natural Language Processing Engineer position at Jobs via Dice?
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Duties: Key Responsibilities:
Design, build, and optimize NLP models using frameworks such as TensorFlow, PyTorch, or Hugging Face.
Develop custom language models for tasks such as text classification, sentiment analysis, named entity recognition (NER), machine translation, and chatbots.
Fine-tune pre-trained models (e.g., BERT, GPT) to meet specific business needs.
Collect, clean, and preprocess large text datasets from diverse sources.
Implement data augmentation techniques to improve model robustness and accuracy.
Develop tokenization strategies and handle multilingual datasets.
Improve the accuracy, efficiency, and scalability of NLP models.
Implement techniques such as transfer learning, reinforcement learning, and domain adaptation.
Conduct error analysis and debug NLP pipelines to ensure optimal performance.
Integrate NLP solutions into existing software platforms and workflows.
Develop RESTful APIs and microservices to expose NLP functionalities.
Collaborate with DevOps teams to ensure smooth deployment and monitoring of models in production environments.
Stay updated on the latest advancements in NLP research and apply relevant innovations to our products.
Contribute to research papers, patents, and internal knowledge-sharing sessions.
Participate in NLP competitions and open-source projects to enhance the company’s visibility in the AI community.
Qualifications:
Undergraduate degree in Computer Science, Data Science, or a related field. Ph.D. is a plus.
Experience in developing NLP models and solutions.
Proficiency in programming languages such as Python, Java, or C Strong understanding of machine learning algorithms, deep learning frameworks, and natural language processing concepts.
Experience with NLP libraries and tools such as NLTK, spaCy, Gensim, and Hugging Face.
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and containerization technologies (Docker, Kubernetes).
Excellent problem-solving skills and ability to work in a collaborative environment.
Experience with multilingual NLP models.
Familiarity with speech-to-text and text-to-speech systems.
Contributions to open-source NLP projects is a plus.
Skills: Recommend 1-3 years experience
Duties: Key Responsibilities:
Design, build, and optimize NLP models using frameworks such as TensorFlow, PyTorch, or Hugging Face.
Develop custom language models for tasks such as text classification, sentiment analysis, named entity recognition (NER), machine translation, and chatbots.
Fine-tune pre-trained models (e.g., BERT, GPT) to meet specific business needs.
Collect, clean, and preprocess large text datasets from diverse sources.
Implement data augmentation techniques to improve model robustness and accuracy.
Develop tokenization strategies and handle multilingual datasets.
Improve the accuracy, efficiency, and scalability of NLP models.
Implement techniques such as transfer learning, reinforcement learning, and domain adaptation.
Conduct error analysis and debug NLP pipelines to ensure optimal performance.
Integrate NLP solutions into existing software platforms and workflows.
Develop RESTful APIs and microservices to expose NLP functionalities.
Collaborate with DevOps teams to ensure smooth deployment and monitoring of models in production environments.
Stay updated on the latest advancements in NLP research and apply relevant innovations to our products.
Contribute to research papers, patents, and internal knowledge-sharing sessions.
Participate in NLP competitions and open-source projects to enhance the company’s visibility in the AI community.
Qualifications:
Undergraduate degree in Computer Science, Data Science, or a related field. Ph.D. is a plus.
Experience in developing NLP models and solutions.
Proficiency in programming languages such as Python, Java, or C Strong understanding of machine learning algorithms, deep learning frameworks, and natural language processing concepts.
Experience with NLP libraries and tools such as NLTK, spaCy, Gensim, and Hugging Face.
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and containerization technologies (Docker, Kubernetes).
Excellent problem-solving skills and ability to work in a collaborative environment.
Experience with multilingual NLP models.
Familiarity with speech-to-text and text-to-speech systems.
Contributions to open-source NLP projects is a plus.
Skills: Recommend 1-3 years experience