Lead Data Scientist

As a Lead Data Scientist in this role, you will be responsible for developing end-to-end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines. You will also build, fine-tune, and evaluate LLM-based models using transformer architectures such as BERT, GPT, T5, and LLaMA. Your role will involve designing and implementing custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies. Additionally, you will develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures. Deployment of models and AI apps using modern MLOps practices across cloud environments, with a preference for Azure, will be part of your responsibilities. Collaboration with product, engineering, and business teams to translate requirements into AI-driven solutions and monitoring model performance for continuous optimization will also be key aspects of your role. Qualifications Required:

• 10+ years of experience in data science with deep hands-on expertise in NLP and Generative AI. - Proficiency in transformer models, embeddings, and modern NLP libraries such as Hugging Face, spaCy, and NLTK. - Strong Python skills with experience in PyTorch/TensorFlow for advanced model development. - Practical experience in building RAG architectures, vector search, and prompt optimization. - Solid understanding of MLOps, model deployment, monitoring, and productionization. - Strong problem-solving abilities with excellent communication and stakeholder engagement skills. If you are interested in this opportunity, you can share your resume for a quick response. (Note: Any additional details about the company were not provided in the job description.) As a Lead Data Scientist in this role, you will be responsible for developing end-to-end NLP and GenAI solutions, including text classification, summarization, RAG systems, conversational AI, and document intelligence pipelines. You will also build, fine-tune, and evaluate LLM-based models using transformer architectures such as BERT, GPT, T5, and LLaMA. Your role will involve designing and implementing custom NLP workflows, embeddings, semantic search, vector databases, and prompt engineering strategies. Additionally, you will develop scalable advanced ML models leveraging deep learning, traditional ML, and hybrid architectures. Deployment of models and AI apps using modern MLOps practices across cloud environments, with a preference for Azure, will be part of your responsibilities. Collaboration with product, engineering, and business teams to translate requirements into AI-driven solutions and monitoring model performance for continuous optimization will also be key aspects of your role. Qualifications Required: