Visa 485 · full work rights · no restrictions · exp. Jan 2029
01 Summary
A results-oriented programmer who takes ownership beyond formal role boundaries,
delivering end-to-end ML and GIS solutions. 16+ years of software
engineering, a PhD in machine learning for hyperspectral remote sensing, and a
product- and deployment-focused mindset.
Machine Learning
PyTorch, hyperspectral, remote sensing, RAG, LLM tool-use, Azure AI Foundry, LangGraph, Azure AI Search, Power Automate, GraphRAG.
GIS & Remote Sensing
ArcGIS Pro, QGIS Desktop/Server, Google Earth Engine.
Built an Azure-native assistant using Azure AI Foundry that lets reviewers interrogate a large document corpus through multi-turn, tool-using LLM conversations.
Owned the whole stack: upload-triggered extraction and comparative-scoring jobs, vector retrieval, containerised deployment, secrets and CI/CD.
Extended cross-document reasoning with GraphRAG and LangGraph behind a streaming chat UI.
Self-hosted ML
Image Classification — Commercial vs Native Vegetation
Owned the problem end-to-end, from labelling strategy through to a deployed, reviewable model.
Tuned iteratively against domain feedback and served it on Ubuntu for ongoing internal use.
GIS
Web GIS Portal & Mobile GIS Apps (Android & iOS)
Delivered interactive GIS layers to office and field users across web, Android and iOS from a single data source.
Worked across the full stack — data, server, apps and test automation — with an AI-accelerated build-and-deploy workflow.
Remote Sensing
Spectral Index Analysis & Comparison System
Lets analysts track vegetation and water conditions over time and compare any two points spatially.
Turns raw satellite archives into decision-ready spectral signals.
IoT
Low-Cost Soil Moisture Sensor Calibration
Calibrated a low-cost capacitive soil-moisture sensor against a research-grade reference.
Co-located DFRobot/ESP32/Raspberry Pi hardware with a SenseCAP S2108, then trained an ML model to map cheap readings onto reference-grade values.
