Currently
Highlights:
- Fine-tuning LLMs (Unsloth & prompt engineering) to generate structured XML outputs.
- Built APIs to connect GUI ↔ Python backends; automated manual validation with AutoHotkey.
- Architected PLC → XML converter (150+ test cases) & fuzzed IoT devices for hardening.
Skills
Selected projects
PLC → PLCopen XML converter
Algorithm + 150+ testcases
LLM-based XML generator
Fine-tuned models for reliable structured output
Automation Suite
700+ manual testcases automated; Python library for tests
IoT Fuzzing
Fuzz scripts using Boofuzz to improve device resilience
Console — recent dev log
➜ booting local dev...
➜ loading LLM weights… done
➜ running 150 validation cases: 150/150 ✅
➜ packaging CLI → .exe: success
➜ pushing prototype to test bench…
➜ loading LLM weights… done
➜ running 150 validation cases: 150/150 ✅
➜ packaging CLI → .exe: success
➜ pushing prototype to test bench…
Technical Expertise
Specialization: Building robust systems at the intersection of industrial automation and AI. Expertise in converting legacy PLC systems to modern XML formats, fine-tuning LLMs for structured outputs, and comprehensive test automation frameworks.
Current Focus: LLM applications for industrial automation, IoT device security through fuzzing, and scalable test automation strategies.
Approach: Data-driven development with emphasis on reliability, comprehensive testing, and production-grade solutions that scale.