red horse

I’m an AI & Machine Learning Engineer specializing in Computer Vision and Generative AI, focused on building end-to-end pipelines and scalable models. I bring a predictive-analytics mindset to deliver high-accuracy, production-ready results.

In my Microsoft Machine Learning Engineer Trainee role, I work on end-to-end Machine Learning pipelines on Microsoft Azure and integrate MLflow for experiment tracking and model management. I implement Prompt Engineering and NLP using Attention Models and Hugging Face Transformers, while developing Computer Vision solutions with transfer learning for image analysis and object detection.

I also connect raw data pipelines to production-ready models using Python, TensorFlow, and advanced preprocessing techniques. I’m preparing for the Azure AI Engineer Associate certification through hands-on lab environments, strengthening both cloud engineering and deployment capability.

My projects reflect my end-to-end ownership: ITLALA delivers a multi-modal fashion recommendation and virtual try-on using LLMs (Gemini), CLIP-based embeddings, and optimized inference. Fridgo combines instance segmentation (YOLACT with Broad FPN) and LLMs (T5, GPT-4) to power food detection and recipe generation with a scalable FastAPI + Streamlit backend.