Pros
🔬 Cutting-edge tech exposure: Worked hands-on with LLMs, especially Gemini 1.5 Pro and OpenAI models, integrating them into data science workflows. 🧠 Real-world projects: Built applications like conversational AI tutors, subtitle search engines, and GenAI content generation tools using LangChain, Streamlit, and FastAPI. 🛠️ End-to-end pipeline experience: From data preprocessing and model training (NLP/CV) to deploying AI apps using Streamlit and FastAPI. 🧑🏫 Great mentorship: Regular sessions with mentors on prompt engineering, LangChain agents, and best practices for AI app development. 🌍 Networking: Collaborative environment with interns and professionals from diverse tech backgrounds.
Cons
⏳ Fast-paced: Requires self-discipline and quick learning, especially when juggling multiple tools and frameworks. 📚 Documentation could improve: Some initial onboarding materials were sparse; had to self-learn a few tools and frameworks.