I'm a final year BTech (ECE) student at NIT Nagaland, driven by a deep curiosity for emerging technologies. My fascination with computers began back in school when I first encountered a basic programming class, where I was amazed by how lines of code could bring ideas to life. Today, I’m diving into AI Agents, LLM integrations, and Data Science, and I’m excited to explore new opportunities to grow in the Data Science field.

• Love experimenting with AI agents, LLMs, and real-time systems — feels like bringing ideas to life.
• Enjoy digging into data to find patterns and build smart, useful stuff.
• Outside tech, I’m into long walks, vibing with friends, sparking spontaneous tech chats, playing cricket, unwinding with music, and exploring curious thoughts.

• 🚀 I'm a curious mind at the intersection of Data Science, AI Agents, and LLM-Oriented Architectures
• 🧠 Currently exploring how Model Context Protocol (MCP) and context-aware systems shape intelligent workflows
• 🤖 I build projects that blend Machine Learning, autonomous agents, and real-time decision pipelines
• 🤝 Always up for collaborations that push the edge of predictive analytics and agent-driven automation
• 📫 How to reach me ganasekharkalla@gmail.com
• ⚡ Fun Fact: I get oddly excited when agents start behaving like tiny problem-solvers — especially if they’re streamlining healthcare or fixing broken data workflows at 2 AM.

🧠 What I’m Exploring

• 🤖 Autonomous agents + real-time decision pipelines
• 🧩 Model Context Protocol (MCP) and LLM-driven systems
• 🏥 AI solutions for healthcare and data infrastructure
• 🔄 Building scalable MLops pipelines with FastAPI, Kafka & Spark

🧠 🌍 Open Source & Community

• MNE-Python: Improved documentation and tutorials to make onboarding smoother for new users.
• Braindecode: Added search + filtering features to help users navigate deep learning model examples.
• Scikit-learn: Contributed cleanup to imports and docs, helping maintain consistency across modules.
• I enjoy contributing small improvements that make tools easier for others — especially in AI, data workflows, and neuroscience tooling.

🛠️ Things I use on daily basis:

• 🤖 Real-Time Fraud Detection System – A streaming fraud detection pipeline powered by Kafka, FastAPI, Docker, Prometheus/Grafana and MongoDB.
  End-to-end detection pipeline with real-time analytics and containerized microservices

• 🏥 Multi-Agent Data Analytics System – A system where multiple agents handle SQL generation, preprocessing, and visual analysis.
  It uses DuckDB + FAISS retrieval and a fine-tuned LLaMA agent for explanations — reducing manual analysis time.

• 🔍 Hybrid Movie Recommendation System – A recommender that mixes collaborative filtering with BERT-based semantic search to improve movie suggestions.
  Served through FastAPI so recommendations work in real time.

👉 Explore more in my pinned repositories above or reach out for code walkthroughs!