We are looking for a Data Scientist with strong expertise in Generative AI, NLP, and deep learning to build intelligent, data driven solutions. The role focuses on developing LLM-powered applications, RAG-based systems, and advanced analytics models to solve complex business problems at scale.
Key Responsibilities Design, develop, and deploy Generative AI and NLP solutions, including LLM fine-tuning and RAG-based architectures. Analyze structured and unstructured data to extract insights and build predictive and generative models. Build and maintain end-to-end ML pipelines, from data collection and feature engineering to model deployment and monitoring. Apply advanced NLP techniques for text classification, summarization, question answering, entity recognition, and conversational AI. Develop and manage embeddings and vector search pipelines for semantic retrieval and knowledge-based systems. Evaluate model performance using appropriate metrics, perform error analysis, and iterate to improve accuracy and robustness. Collaborate with product, engineering, and data teams to translate business requirements into data science solutions. Communicate findings, model behavior, and insights clearly to both technical and non-technical stakeholders. Stay current with emerging trends and research in Data Science, Generative AI, LLMs, and NLP.
Required Skills & Qualifications Strong foundation in Data Science, Machine Learning, and Deep Learning. Hands-on experience with Generative AI and LLM fine-tuning (e.g., prompt engineering, LoRA, PEFT). Experience building RAG-based systems and working with unstructured text data. Solid knowledge of NLP and transformer-based models (BERT, GPT, T5, LLaMA, etc.). Proficiency in Python, along with ML/DL libraries such as PyTorch, Tensor Flow, scikit-learn. Experience with vector databases (FAISS, Pinecone, Weaviate, Milvus). Strong skills in data analysis, model evaluation, and experimentation. Familiarity with cloud platforms (AWS, Azure, or GCP). Strong analytical thinking and communication skills.
Good to Have Experience with MLOps (model versioning, monitoring, CI/CD). Knowledge of distributed computing or large-scale data processing. Experience handling large, unstructured, or semi-structured datasets. Understanding of AI ethics, bias detection, and responsible AI practices.
