Nitish R. G. is a Data Science and AI Practitioner currently studying at the Indian Institute of Technology, Madras. They are also involved with the Global Data Intelligence Network (GDIN). Nitish has experience in technical architecture and developer marketing, and is seeking knowledge sharing, community, talent, co-founders, and founding engineers. Their current projects include a RAG pipeline, face emotion detection, and end-to-end ML workflows. Nitish is also working on a React/Vite-based iPortfolio and contributes to GDIN with a focus on Python, scikit-learn, FastAPI, and containerized deployments. They are open to introductions and prefer contact via email.

Technical architecture, developer marketing, knowledge sharing with peers, community and friendships, talent, co-founders (technical), founding engineers, RAG pipelines, LangChain, HuggingFace, Ollama, vector storage and ingestion, deep learning, computer vision, end-to-end ML workflows, Python, scikit-learn, FastAPI, containerized deployments, and open-source contributions.

Current projects include **discourse-rag-assistant**, a RAG pipeline utilizing LangChain, HuggingFace, and Ollama with Docker Compose for vector storage and ingestion. **FACE-EMOTION-DETECTION** uses OpenCV and deep learning, while **FUTURE_DS_01–03** involves end-to-end ML workflows in Jupyter. He also maintains a React/Vite-based **iPortfolio** and contributes to the **Global Data Intelligence Network (GDIN)**, focusing on Python, scikit-learn, FastAPI, and containerized deployments.

Safwan Ahmad Saffi is an AI/ML Trainee at NETSOL, specializing in Generative AI, Agentic AI, and LLMs. He is a final-year Computer Science student from Pakistan with a strong technical background, demonstrated by his participation in NASA and Shell.ai hackathons and his selection for Stanford’s Code-In-Place program. Safwan also serves as an ICSC ambassador and is proficient in Git/GitHub workflows. He is currently open to new opportunities and his expertise lies in functional prototyping and hands-on building within AI/ML ecosystems, with a focus on technical architecture.

Generative AI, Agentic AI, Large Language Models (LLMs), Technical Architecture, AI/ML workflows, functional prototyping, software development, open-source contribution, collaborative coding.

Currently serving as an AI/ML Trainee at NETSOL, focusing on the development and implementation of Generative AI, Agentic AI, and Large Language Models (LLMs). Tactical work includes building functional prototypes and designing technical architectures for AI workflows. Recent activity involves competing in the NASA and Shell.ai hackathons and contributing to Stanford’s Code-In-Place program, emphasizing hands-on coding, version control with Git, and collaborative software development.