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NLP Engineer

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Onsite

NLP Engineer

Krazy Mantra It

NLP Engineer

Skills

NLP / Natural Language Processing

2 years ago

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Important information


Contract type:

Permanent

Location:

Pune, Maharashtra, India

Work mode:

Onsite

Published on:

14 October 2026

What they need


Krazy Mantra Group of Companies is looking for NLP Engineer to join our dynamic team and embark on a rewarding career journey.
We are seeking a talented and experienced Natural Language Processing (NLP) Engineer to join our innovative team. The NLP Engineer will play a crucial role in developing and implementing cutting-edge NLP solutions for various applications, such as natural language understanding, sentiment analysis, and language generation. The ideal candidate will have a strong background in NLP, machine learning, and programming. Responsibilities : NLP Model Development : Design, develop, and implement NLP models and algorithms for diverse applications. Collaborate with data scientists and software engineers to integrate NLP solutions into products and services. Text Data Processing : Preprocess and clean large volumes of text data for NLP model training. Implement techniques for feature extraction and representation from text data. Model Training and Evaluation : Train and fine-tune NLP models using machine learning frameworks. Evaluate model performance, identify areas for improvement, and implement enhancements. Named Entity Recognition (NER) : Implement NER systems to extract entities and relationships from unstructured text. Optimize NER models for accuracy and efficiency. Sentiment Analysis : Develop sentiment analysis models to understand and classify opinions expressed in text. Enhance sentiment analysis algorithms to handle nuanced expressions. Language Generation : Build language generation models for tasks such as chatbots, content creation, and dialogue systems. Ensure generated content adheres to desired language styles and context. Stay Current with NLP Trends : Keep abreast of the latest advancements in NLP research and technologies. Apply state-of-the-art techniques to improve NLP capabilities. Documentation : Create and maintain comprehensive documentation for NLP models, algorithms, and implementations. Collaborate with cross-functional teams to share knowledge and best practices.

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