Data Scientist

Job Description

We are seeking a highly skilled and innovative Data Scientist with a strong focus on Generative AI, NLP, and Large Language Models (LLMs). The ideal candidate will design, develop, and deploy end-to-end data science solutions that harness the power of advanced ML, deep learning, and Gen AI technologies to drive real-world impact. Key Responsibilities: Design and implement scalable data science solutions using Generative AI, NLP, and ML techniques. Develop, fine-tune, and evaluate Large Language Models (LLMs) such as GPT, BERT, and similar architectures. Analyze structured and unstructured data to generate actionable insights for business problems. Collaborate cross-functionally with engineering, product, and business teams to integrate AI models into production systems. Conduct cutting-edge research in Gen AI and deep learning; evaluate and apply recent advancements. Build and maintain robust pipelines for model training, evaluation, monitoring, and deployment. Communicate complex technical findings clearly to technical and non-technical stakeholders. Required Skills & Qualifications: Strong hands-on experience with Generative AI, LLMs (e.g., OpenAI, Hugging Face Transformers, etc.). Proven expertise in core NLP tasks: text classification, summarization, named entity recognition, sentiment analysis, etc. Proficient in Python and related libraries: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch. Experience developing, validating, and deploying ML models in production environments. Familiarity with vector databases (e.g., FAISS, Pinecone), embeddings, and semantic search. Exposure to cloud platforms like AWS, Azure, or GCP, and MLOps tools/workflows (CI/CD, model monitoring, etc.). Preferred Qualifications: Experience in prompt engineering, Retrieval-Augmented Generation (RAG), or fine-tuning/customizing LLMs. Contributions to AI research papers, open-source projects, or participation in ML competitions (e.g., Kaggle) is a plus. Knowledge of responsible AI practices, model interpretability, and bias mitigation techniques.