Machine Learning Engineer Sydney, Australia

I build AI systems that make it into the real world.

Production machine learning, computer vision, and agentic systems, grounded in careful research and measured by what they make possible.

Read my resume
Research to productionScroll to explore

A little context

Research-minded.
Production-focused.

I am a machine learning engineer focused on turning research into dependable AI products. At IHX, I re-engineered a document-intelligence product supporting around 90% of company revenue, reaching 99% table-classification precision and cutting pipeline time by half.

At the University of Technology Sydney, I research mechanistic interpretability for EEG foundation models. My work spans model evaluation, sparse feature analysis, computer vision, multimodal systems, and production AI engineering.

I build end-to-end systems, from data and model experiments through APIs, observability, and deployment. I also write about ideas and implementations I am exploring in the blog.

Selected work / 2025-26

Projects.

Browse GitHub

Tools and methods

What I work with.

From model research and evaluation to the APIs, workflows, and monitoring around a production system.

Agentic AI & LLM Engineering

LangGraphLangChainMulti-agent systemsAgent memoryStructured outputsPydanticOpenRouterOpenAI APIGemini API

Production AI & MLOps

PythonFastAPIREST APIsBatch and real-time inferenceGitGitHub ActionsLinuxAWSGCPGrafana

Deep Learning & Computer Vision

PyTorchTensorFlowHugging Face TransformersLoRADonutLayoutLMv3DETRTable TransformerYOLOOpenCVOCR

Evaluation, Data & Research

Model evaluationLangSmithCost-aware model routingNumPypandasSciPyRAGQdrantSupabaseSQLiteMechanistic interpretability