Data Scientist

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.