Computer vision & inference
Professional experienceMulti-stage vision pipelines, OCR, detection, segmentation, tracking, inference optimisation, and edge deployment.
I’m an AI engineer focused on turning models into practical systems that solve real problems. I enjoy the work beyond the model, connecting data, building pipelines, and making everything run reliably in the real world.
My background is in computer vision, where I’ve worked across object detection, segmentation, OCR, and zero-shot models. What drives me most is deploying these models in real-world environments and making them perform reliably under challenging conditions such as unstable cameras, low-resolution footage, and limited computing resources.
A core part of my work focuses on optimizing inference for edge devices. Using tools like TensorRT, I reduce latency and tailor pipelines to the target hardware while keeping results accurate and consistent in demanding conditions.
Building on this foundation, I’m now developing an AI-powered real estate assistant for the UAE market. The system combines language models, retrieval-augmented generation (RAG), and tool calling to deliver accurate answers grounded in property transaction data and official documents.
What I’ve worked with.
What I’m exploring next.
Multi-stage vision pipelines, OCR, detection, segmentation, tracking, inference optimisation, and edge deployment.
Building databases and indexes, and developing well-maintained RAG pipelines for data preparation, retrieval, and grounded answers.
Exploring LLM applications, tool calling, and agent workflows with LangChain and LangGraph.