[User Intent] ──▸ [Agentic Coordinator] ──▸ [Tool Orchestration] ──▸ [Memory Stream] │ [Optimized Execution] <───────(Reasoning Loop)──────────────────────────────┘

• ▸ Agentic Architectures: Developing stateful, autonomous AI agents utilizing reasoning patterns (ReAct), multi-agent orchestration, and specialized execution tools.
• ▸ LLM Infrastructure: Engineering high-velocity RAG (Retrieval-Augmented Generation) loops using LangChain and OpenAI to feed precise data streams back into active contexts.
• ▸ High-Speed Vector Fields: Scaling similarity search nodes via Pinecone and ChromaDB to achieve sub-second document convergence.
• ▸ MLOps Integration: Building automated execution pipelines that continuously ingest, validate, and deploy production-grade intelligence.

SolAgent Autonomous AI agent designed to manage Solana crypto wallets using natural language processing.	MeshFix Intelligent automated tool optimization pipeline for data restructuring and geometric normalization.
Study Bot Intelligent assistant built with Google SDK for AI-driven insights.

	:: The Universal Port for AI: A Deep Dive into MCP Architecture An architectural investigation into the Model Context Protocol (MCP), establishing standardized data ports to seamlessly connect LLMs with external tools, applications, and memory environments.
	:: How Do You Know Your AI is Actually Good? A Guide to LLM Evaluation Breaking down rigorous testing frameworks, metric formulations, and automated benchmarking techniques required to validate LLM performance and alignment in production environments.
	:: Teaching AI to Remember: A Deep Dive into Retrieval-Augmented Generation Exploring the high-velocity mechanics of RAG systems, analyzing how dynamic index retrieval bridges foundational model knowledge with external, real-time database contexts.

• AWS AI Practitioner Challenge — Udacity (2026)
• AI Engineer for Data Scientists Associate — DataCamp (2024)
• Python Data Associate — DataCamp (2023)