I'm Prasanna Vijay
Iβm an AI & IoT engineer-in-progress with a strong focus on building autonomous AI agents that can reason, learn, and act in real-world environments. My work blends agentic AI architecture, LLMOps, and IoT-based sensing systems, creating solutions that are both intelligent and production-ready.
From integrating multi-agent systems with persistent memory and tool-use capabilities, to optimizing low-power IoT data pipelines for environmental monitoring, I specialize in turning AI concepts into deployable, scalable products.
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#π Current Focus
β’ Agentic AI Engineering β Designing multi-agent frameworks with OpenAI SDK, Azure integration, MCP servers, and PostgreSQL context stores.
β’ LLMOps β Building pipelines for LLM applications, memory management, and tool integration at scale.
β’ RAG-based Conversational Systems β Creating retrieval-augmented generation pipelines with domain-specific knowledge bases.
β’ IoT & Edge AI β Deploying AI-enabled LoRaWAN sensor networks for environmental and field applications.
β’ Cloud Deployment β Dockerized microservices, async event loops, and REST API orchestration for AI agents.
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#π‘ Notable Projects
β’ Conversational RAG Agents β Multi-agent Azure deployment with structured reasoning, REST API interface, and PostgreSQL-powered memory.
β’ MCP-Integrated Agent Framework β Remote MCP server list and tool orchestration for AI agent task execution.
β’ Forest Monitoring AI Dashboard β LoRaWAN + AI data insights for NGOs, animated dashboard with real-time updates.
β’ Traffic Flow & Incident Prediction β YOLO + LSTM + clustering for intelligent transport system decision-making.
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#π Skills Snapshot
β’ AI/ML β Python, LLMOps, MLOps, NLP, Computer Vision, RAG, Agentic AI
β’ Cloud & Infra β Azure, Docker, Kubernetes (learning), AsyncIO pipelines
β’ IoT & Embedded β LoRaWAN, RAK4630 ecosystem, sensor fusion, binary payload transmission
β’ Data β PostgreSQL, data streaming, API-based integrations
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#π― Career Goals
To specialize as an Agentic AI Engineer, building adaptive, autonomous systems that combine reasoning, planning, and execution for real-world use cases β with a strong focus on scalability, reliability, and human-AI collaboration.
