Hello, I'm Dineth

I'm an AI/ML systems engineer building end-to-end systems across machine learning, data engineering, software, and edge computing.

My work connects models, data, and hardware. I build MLOps and analytics pipelines, GPU and TPU architecture simulators, embedded IoT platforms, and applied AI systems that are understandable, testable, and useful beyond the demo.

Portfolio · LinkedIn · X · Medium · Substack

What I build

• Parallel and accelerator computing: TinyGPU makes SIMT execution, memory, and synchronization visible in Python; TinyTPU runs a SystemVerilog systolic array in the browser through WebAssembly.
• Machine learning and data engineering: the Telco Churn MLOps Pipeline combines MLflow, Spark, Kafka, Airflow, and Dockerized inference; the Airbnb Market Intelligence Pipeline focuses on reproducible analytics with DuckDB, Parquet, data-quality checks, statistics, and machine learning.
• Edge and applied AI: the Smart Beehive Monitoring System connects solar-powered ESP32 sensing to Firebase and a React/TypeScript dashboard; the Multi-Omics Cancer Subtype Classifier applies XGBoost, PyTorch, biological pathways, SHAP, and integrated gradients to cancer subtype classification.

Focus

AI/ML and MLOps · Data and software systems · Parallel and edge computing

Python · PyTorch · scikit-learn · SQL · MLflow · Apache Spark · Kafka · Airflow · FastAPI · Docker · C++ · SystemVerilog

Elsewhere

I write about practical AI/ML systems, parallel computing, and embedded engineering on Medium and Substack. For collaboration, engineering opportunities, or technical discussion, contact me through LinkedIn or email.