Physics-trained AI Engineer building measurable, production-minded systems across LLM fine-tuning, RAG, knowledge graphs, NLP, and computer vision - from model evaluation to APIs and observability.
Physics-trained AI Engineer focused on end-to-end applied AI: LLM fine-tuning, retrieval-augmented generation, knowledge graphs, NLP, computer vision, and predictive machine learning.
I turn experiments into measurable systems using rigorous evaluation, vector databases, FastAPI, Docker, and observability - with clear evidence of quality, latency, and reliability.
Alongside technical delivery, I founded and led Sufra Alqalam, a 110-member initiative awarded 5,000 JOD by the Jordanian Ministry of Culture.
22 projects across LLM fine-tuning, retrieval, knowledge graphs, NLP, computer vision, predictive ML, and AI infrastructure - organized by specialization for fast review.
Flagship work first: a reproducible domain fine-tuning lifecycle and a collaborative agentic platform with controlled actions.
Retrieval systems ordered from full multimodal RAG through focused experiments in hybrid search, routing, re-ranking, and document QA.
Schema-aware systems for translating language into graph queries, validating claims, combining graph structure with vectors, and querying RDF knowledge.
Production-oriented AI infrastructure covering service routing, failure isolation, distributed tracing, metrics, dashboards, and load profiling.
Evaluation-first NLP work and an applied reasoning pipeline that converts large-scale customer feedback into structured recommendations.
Applied deep-learning work for domain-specific object detection and renewable-energy forecasting, each backed by measured outcomes.
Classical machine-learning projects centered on honest evaluation, tuning, reproducible comparison, and interpretable performance diagnostics.
Seeking AI Engineer roles where I can build and evaluate reliable LLM, retrieval, NLP, computer vision, and production ML systems. The fastest ways to reach me are below.