Data Scientist – Generative AI & NLP

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.

• 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
  .

• 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.

• 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
  .