I build AI systems that understand, refactor, and operate complex software systems.
🔧 Agentic Refactoring Systems
AI agents that detect and remediate software anti-patterns in large codebases.
🧠 Legacy Code Intelligence
LLM-powered pipelines that reverse engineer and migrate legacy enterprise systems.
⚙️ Distributed AI Infrastructure
Building scalable AI systems with containerized services, vector databases, and observability.
Agentic system that scans Java repositories, detects architectural anti-patterns, and proposes automated refactoring strategies.
Stack
LangGraph • LangChain • WatsonX • vLLM • Static Analysis
Multi-agent system converting COBOL systems into modern Python services.
Capabilities:
- Reverse engineering legacy systems
- Structured requirement extraction
- Python code generation
- Formal verification workflows
Python library for computing topological indices in molecular graphs.
Features:
- NetworkX-based graph modeling
- SMILES support
- Zagreb / Wiener / Hyper-Wiener indices
Self-hosted hybrid vector database designed for LLM workflows.
Architecture:
FAISS + SQLite
LangChain compatible
Local embedding pipelines
Distributed Dask-based system for running large-scale quantum circuit simulations.
Features:
- Parallel QASM execution
- Distributed compute cluster
- Quantum ML experimentation
⚡ I enjoy building systems that push AI from demos into real engineering infrastructure.


