Generative AI and Natural Language Processing (NLP)

Job Description

Must Have Skills: Basic understanding of Natural Language Processing (NLP) techniques such as text classification, tokenization, sentiment analysis, and named entity recognition (NER). Familiarity with Generative AI concepts, including Generative Adversarial Networks (GANs), transformers, and autoencoders. Strong programming skills in Python, with experience using libraries like NumPy, Pandas, TensorFlow, PyTorch, and Hugging Face for model building and data manipulation. Knowledge of machine learning algorithms and basic model evaluation techniques. Familiarity with cloud platforms (e.g., AWS, Azure, Google Cloud) or basic cloud concepts for model training and deployment. Willingness to learn and work with GPU/TPU computing for efficient model training and inference. Natural Language Processing (NLP): a. Assist in the design and development of NLP systems for tasks such as text classification, sentiment analysis, named entity recognition (NER), and machine translation. b. Contribute to the development of language models for text summarization, question answering, and dialog systems. c. Work with large, unstructured text datasets and apply techniques such as text preprocessing, tokenization, and embedding generation. Collaborate with team members to develop AI-driven solutions that involve transformer-based models, text generation, and other cutting-edge NLP techniques. Learning & Growth: Continuously improve knowledge of emerging AI and NLP trends, techniques, and technologies. Participate in learning programs and research to deepen expertise in transformer models, LLMs, RAG, and other relevant techniques. Documentation: Maintain clear and organized documentation of models, experiments, code, and results to ensure reproducibility and efficient knowledge sharing within the team. Nice to Have Skills: Familiarity with Agentic AI concepts or exposure to intelligent agent-based systems. Exposure to working with open-source LLMs (e.g., Hugging Face) and transformer-based models. Understanding of Docker for containerizing applications and models. Exposure to AutoML frameworks or transfer learning techniques. Familiarity with version control systems like Git. Understanding of basic MLOps concepts for deploying machine learning models in production. Interest in AI deployment tools such as Flask, FastAPI, Django etc . for serving models in real-world applications. Soft Skills: Strong verbal and written communication skills with the ability to work well in a team. Strong customer focus, ownership, urgency, and drive. Ability to handle multiple, competing priorities in a fast-paced environment. Work well with the team members to maintain high credibility. Educational Requirements (any of the following): Bachelor?s degree in computer science, Engineering, Mathematics, Physics, or a related field (required). Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field (preferred for senior roles). Location: India