About me

I am a Research Engineer specializing in Agentic AI frameworks. My work focuses on developing next-generation LLM AI agents that can reason, plan, and act autonomously by leveraging cutting-edge LLM architectures, tool integration, and multi-agent coordination.

My Expertise

• LLM-Based AI Agents – Building intelligent agents that interact with environments, automate workflows, and make decisions in dynamic settings.

• Reasoning & Planning – Implementing techniques like ReAct (Reason + Act), chain-of-thought (CoT) prompting, and hierarchical decision-making to create adaptive AI agents.

• Tool & API Integration – Enhancing LLMs with external tools, APIs, databases, and autonomous execution pipelines to extend their problem-solving capabilities.

• Multi-Agent Systems – Developing collaborative AI agents that communicate and coordinate in decentralized or distributed environments.

• Ontologies & Knowledge-Based AI – Designing AI agents that leverage structured knowledge representations, ontologies, and knowledge graphs to enhance reasoning, contextual understanding, and decision-making.

• Retrieval & Embedding-Based AI – Implementing embedding models, vector stores, retrievers, and Retrieval Augmented Generation (RAG) techniques to enhance AI systems with efficient data representation, search capabilities, and knowledge retrieval.

• Fine-Tuning & Optimization – Adapting LLMs for domain-specific tasks with prompt tuning, reinforcement learning, and performance optimization techniques.

• Evaluation & Testing – Assessing AI applications’ performance and effectiveness by benchmarking responses against predefined criteria while verifying that components and integrations work as expected, ensuring reliability and stability.

With a strong foundation in AI research, deep learning, and software engineering, I strive to push the boundaries of autonomous AI by building robust, efficient, and scalable LLM-driven agents.