I turn messy datasets into meaningful narratives—and scalable systems. With a foundation in machine learning and statistical modeling, and a current obsession with LLMs, agents, and AI orchestration, I sit at the intersection of data science, architecture, and applied intelligence. In my early days, I honed my skills building end-to-end ML models—regression, classification, clustering, deep learning. Now, I architect systems where those models live, breathe, and interact autonomously. Think pipelines that think for themselves, agents that know when to call an API and when to ask for help. Currently immersed in: N8N : (If you are reading this , do try n8n, This tool is love) Agentic frameworks (LangChain, AutoGen, CrewAI) RAG architectures for knowledge-augmented decision-making Multi-agent collaboration for solving complex workflows (MCP, A2A, ACP, ANP) LLMOps: because prompt engineering is just the beginning From clinical trial optimization to real-time Customer Analytics, I help businesses to move from raw data to real-world impact—with intelligence that scales and adapts. 🔧 Core Stack: Python, PyTorch, Hugging Face, LangChain, SQL, Docker, FastAPI, GCP/AWS, Kubernetes, and a few secret sauces. What excites me? → Architecting autonomous agents that do, not just predict. → Building systems that collaborate, not just compute. → Using data to solve problems that actually matter. I’m always open to exchanging ideas, collaborating on high-impact projects, or just nerding out over the latest in LLMs and intelligent systems. Whether you’re exploring GenAI use cases, rethinking your ML architecture, or building something bold—let’s talk. Drop a message. Let’s build the future.