Artificial Intelligence (AI) Internship

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

1. Research and Implementation: Study and implement findings from cutting-edge research papers on large language models (LLMs), retrieval-augmented generation (RAG), and other AI technologies.
2. Benchmarking and Evaluation: Assess the performance of state-of-the-art models to ensure they meet desired benchmarks and standards.
3. Model Development and Fine-Tuning: Engage in the development and fine-tuning of models using Prompt Engineering and Fine-Tuning (PEFT) methods to enhance model performance on text, vision, audio, and video data.
4. Deployment and Productionizing: Take part in the deployment processes to productionize large models ensuring they are scalable and efficient.
5. Training and Maintenance: Manage the training cycles of AI models, ensuring high accuracy and performance continuity.
6. Framework Setup: Establish frameworks for efficient training, testing, and deployment of models to streamline development processes.
7. Full-Stack Development: Prototype and develop full-stack Gen AI solutions integrating LLMs and RAG into functional applications.
8. Data Analysis: Perform data analysis to extract insights and improve model performance based on empirical data.
9. AI System Evaluation: Systematically evaluate AI systems for performance and reliability across various parameters.
10. Cloud and API Integration: Utilize APIs (both open-source LLMs and company-provided paid APIs) and cloud platforms like Google Cloud and Azure for developing and hosting AI applications.

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