Experience

Generative AI Engineer

• -Leading the rebuild of CoreReports (CoreReportsV2), an AI-powered insurance reporting and Text-to-SQL platform for Doha Insurance Group
• -Architected the model stack around Qwen3.8 (27B via NVIDIA NIM in production, 12B via Ollama for prototyping) with Qwen3Guard-4B for input screening
• -Validated generation against nine governed Oracle views on the live production schema
• -Stood up a dedicated orchestration cluster (LangGraph, Supabase/pgvector, Redis, LangFuse, FastAPI) across a shared compute pool for production and evaluation pipelines

AI R&D Apprentice

• -Worked with AI scientists on EdTech solutions, including an MVP autograding system spanning multiple grades and subjects
• -Benchmarked Mistral Document AI against Gemini for Arabic-language exam PDF extraction and handwritten-text recognition across student age groups
• -Recommended Gemini as the primary pipeline with Mistral as a cost-efficient complement

AI Engineer

• -Designed and optimised agentic AI workflows to streamline and accelerate automated task processing using Python and PyTorch
• -Collaborated with international engineering teams to integrate state-of-the-art AI functionalities into Deriv's product environments

AI Engineer

• -Founding member of Barzan's AI & Data Analytics department; helped build it from the ground up
• -Built a Text-to-SQL engine translating natural language into optimised SQL for business analytics automation
• -Engineered an automated document classification pipeline using NLP and deep learning, increasing throughput by 300% and reducing manual intervention by 85% on high-volume insurance data
• -Implemented an unsupervised anomaly detection framework achieving 92% fraud detection accuracy across 2M insurance transactions, reducing investigative workload by 65%

Research Assistant

• -Researched applications of machine learning in the medical field under Dr. Mousa Al-Akhras; prepared documentation, conducted literature reviews, and presented findings using Matplotlib and Pandas
• -Analysed over 50,000 records of experimental data using SQL and advanced Excel formulas, improving data retrieval efficiency by 20% for faculty-led research initiatives
• -Streamlined data collection workflows by automating data entry with Python scripts, increasing processing throughput by 35% across three research projects

Machine Learning Engineer Intern

• -Engineered end-to-end machine learning pipelines integrating data preprocessing, model training, and automated evaluation for client-facing applications
• -Collaborated with a team of 5 engineers to maintain continuous model performance monitoring, minimising analytics downtime to under 1% across web and mobile platforms