Aryan Prasad

Aryan Prasad is an emerging AI and machine learning engineer with a robust foundation in algorithmic modeling, statistical learning, and symbolic reasoning. His expertise encompasses retrieval-augmented generation (RAG), vector similarity search, and autonomous agent orchestration. Aryan is proficient in advanced AI frameworks, including LangChain for modular LLM architecture, ChromaDB and Qdrant for high-dimensional vector indexing, and CrewAI for multi-agent task execution. He has also gained experience with cutting-edge frameworks such as Mem0 for scalable long-term memory integration, Agno for lightweight, high-performance agent development, LangGraph and AutoGen for customizable multi-agent communication protocols. With strong programming skills in Java, Python, Flask, and RESTful API development, he builds and deploys end-to-end AI systems with real-time data interfacing. In the embedded AI domain, Aryan has engineered hybrid inference systems that fuse image recognition with environmental sensor analytics using supervised regression models and time-series forecasting. His projects integrate data streams from MQ-series gas sensors and DHT22 modules to model spoilage dynamics and environmental quality. Utilizing Raspberry Pi and Arduino microcontrollers, he prototypes edge-deployable intelligent systems with real-world IoT integration, sensor calibration, and embedded signal processing demonstrating proficiency in both hardware interfacing and lightweight ML deployment. Currently pursuing a Bachelor of Engineering (BE), Aryan has been recognized through multiple hackathon victories for building technically robust, cross-domain AI solutions. His technical interests span autonomous agent frameworks, embedded ML, sensor fusion, real-time inference, and hybrid neuro-symbolic architectures. He is passionate about architecting interpretable and scalable AI systems that bridge the gap between experimental research and real-world impact particularly in domains requiring edge intelligence, automation, and decision support.