Data Science Internship

Selected intern's day-to-day responsibilities include:

1. Work with Large Language Models (LLMs) such as GPT, LLaMA, or Mistral to build real-world applications.
2. Develop effective prompts to improve AI-generated outputs.
3. Implement Retrieval-Augmented Generation (RAG) for better knowledge retrieval.
4. Apply classic ML techniques (classification, regression, clustering) using scikit-learn, TensorFlow, or PyTorch.
5. Work with Hugging Face Transformers for pre-trained model adaptation.
6. Develop RESTful APIs to integrate AI models into applications.
7. Work with APIs such as OpenAI, Cohere, Hugging Face, Google AI for model interactions.
8. Implement LangChain or similar frameworks for LLM-driven applications.
9. Deploy AI apps using Flask, FastAPI, or similar backend frameworks.
10. Use lightweight frontend technologies such as Streamlit, Gradio, etc.
11. Collect and preprocess structured and unstructured data for ML training.
12. Apply NLP techniques (tokenization, named entity recognition, text summarization).
13. Good understanding of SQL querying and PySpark.
14. Deploy models using Docker and cloud platforms (AWS, GCP, Azure).
15. Maintain clear documentation of workflows, experiments, and findings.
16. Share insights with the team on emerging AI techniques and tools.

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