Professional Overview
Machine-learning engineer (signal + computer vision) focused on the evaluation that decides whether a model holds on real, shifted data. I build ML end to end — modeling, pipelines, deployment (PyTorch, ONNX). Mostly acoustic signal with CV detection alongside; now ramping into medical imaging — cardiac-MRI segmentation (systole, on GitHub)
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
