Architecting the Future of Agentic Intelligence

Specializing in Hybrid RAG, Multi-Agent Orchestration, and Memory-Efficient LLM Optimization.

Professional Summary

Senior AI Engineer focused on bridging the gap between raw Large Language Models and production-grade intelligent systems. I specialize in building Hybrid RAG architectures that combine the semantic power of Vector Databases with the deterministic structure of Knowledge Graphs. My work centers on creating Agentic AI frameworks that execute complex, multi-step workflows with precision.

Core Expertise

Agentic AI & Orchestration

  • Designing collaborative agent frameworks using LangGraph and CrewAI.
  • Building robust control planes for complex AI workflows and state management.
  • Implementing sophisticated tool-selection logic for seamless API integration.

Advanced RAG & Hybrid Search

  • Architecting solutions that unify Knowledge Graphs and Vector Search.
  • Utilizing state-of-the-art NLP for query expansion and re-ranking.
  • Developing end-to-end RAG pipelines for enterprise scalability.

Memory Efficiency & LLM Optimization

  • Researching algorithms to reduce memory footprint of RAG processing.
  • Fine-tuning models specifically for RAG-awareness and noise reduction.
  • Optimizing inference pipelines for high-throughput, low-latency responses.
  • Training specialized heads for massive context window management.

Current Research

Global AI Pipeline Standard

Creating an open-source framework to standardize RAG and Agentic data flows across the ecosystem.

Memory-Efficient RAG

Training specialized heads within LLMs to handle massive context windows without linear memory growth.

Knowledge Graph Synthesis

Automating Graph creation from unstructured text to enhance Hybrid RAG depth.