NLP and LLM Engineer

NLP and LLM Engineer Location: Chennai / Pune / Thanjavur / Hybrid Type: Full-Time Reports to: Lead Data Scientist. Position

: We are seeking an experienced NLP and LLM Engineer to join our innovative team. The ideal candidate will have a strong background in natural language processing, machine learning, and deep learning, with a specific focus on developing and optimizing large language models. You will be responsible for designing, building, and deploying advanced NLP solutions to solve complex real-world problems.

: • Model Development: • Design, develop, and fine-tune large language models (LLMs) to enhance their performance on specific tasks such as question answering, summarization, and instruction tuning. • Implement Retrieval-Augmented Generation (RAG) techniques to improve model responses using external knowledge sources. • Apply Reinforcement Learning from Human Feedback (RLHF) to refine model outputs and ensure alignment with user preferences. • Conduct research and development on transformer architectures, attention mechanisms, and other state-of-the-art NLP methodologies. • Develop and optimize models for tasks such as text classification, summarization, entity recognition, sentiment analysis, and machine translation. • Data Management: • Collect, preprocess, and analyze large datasets for training and evaluating NLP models. • Utilize vector databases to manage and retrieve high-dimensional data efficiently. • Implement data augmentation techniques to enhance training datasets and improve model robustness. • Deployment and Maintenance: • Deploy NLP models into production environments, ensuring scalability and robustness. • Monitor and maintain deployed models, making improvements as needed based on performance metrics and user feedback. • Develop and maintain APIs and microservices for integrating NLP models into applications. • Research and Innovation: • Stay up-to-date with the latest research in NLP and machine learning. • Experiment with advanced techniques like instruction tuning to improve model performance on specific tasks. • Contribute to the research community through publications, presentations, and participation in relevant conferences. • Explore and implement techniques like zero-shot, few-shot learning, and transfer learning to enhance model adaptability. • Collaboration: • Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to integrate NLP solutions into products and services. • Collaborate with engineering team to develop scalable solutions for complex NLP challenges.