Machine Learning Engineer specializing in LLM optimization, distributed ML systems, and deploying AI solutions at scale. Building the future of intelligent applications.
Explore My AI Projects →Optimized Falcon model inference using Pipeline Parallelism and CUDA graph optimization
PerformanceWinner at IEEE Metaverse-2022 Conference for innovative blockchain authentication framework
RecognitionCore contributor to the renowned Falcon LLM series, deployed by millions worldwide
ImpactBuilt high-performance retrieval systems with industry-leading accuracy metrics
PrecisionTop 10% student with 3.8/4.0 GPA in Master's of Machine Learning program
AcademicAwarded UAE Golden Visa for exceptional talent in AI and technology innovation
AchievementLeading edge research in LLM optimization and distributed ML systems
ML Engineer specializing in production-ready AI solutions at scale
UAE Golden Visa holder recognized for exceptional talent in AI technology
Daniel is an Applied Scientist at Inception, a G42 company based in Abu Dhabi, UAE. His work revolves around Agentic RAG systems, AI agent evaluations, and Edge AI - building scalable machine learning systems that bridge the gap between theoretical advancements and practical applications.
He holds an MSc in Machine Learning from MBZUAI and a BSc in Information Technology from Zayed University. Previously, he worked as a Machine Learning Engineer at Technology Innovation Institute/AICCU center, contributing to several cutting-edge projects in LLMs, RAG systems, model compression, and edge inference optimization.
Daniel finds joy in building AI-native systems and isn't just about theory—he's a builder! As a UAE Golden Visa holder, he's passionate about contributing to innovative AI projects that make a real impact, always eager to connect with like-minded professionals and organizations at the forefront of technology and research.
When he's not playing with models, you'll find him on epic phone calls with family or lost in football—kicking it on the field or glued to the game on TV! ⚽
Built comprehensive evaluation framework using DeepEval, RAGAS, and TruLens with 50+ metrics across 4 specialized agents.
Achieved up to 22x throughput improvement using tensor/pipeline parallelism across multiple GPUs for Falcon and Llama models.
Built LLM compression pipeline with depth + width pruning, healing using LORA finetuning. 19% reduction, 96% performance.
Deployed RAG systems (RAGFLOW and DIFY) on AWS with API integration. Automated evaluation achieving 95% faithfulness.
Developed Flutter mobile app to run quantized .gguf LLM models locally on mobile devices with efficient on-device inference.
Implemented data and tensor parallelism with DeepSpeed on 4 H100 GPUs, reducing training time and increasing throughput by 34%.
Developed a search engine utilizing LLM and vector space models for efficient document retrieval with advanced semantic understanding.
Mathematical foundations, Statistics, Linear Algebra, Probability Theory
Production-ready AI systems, Edge deployment, Agentic systems, Real-world impact
A journey of continuous learning and recognition in AI & Technology
UAE Ministry of Presidential Affairs
Awarded prestigious merit scholarship for outstanding academic performance and leadership potential in technology and innovation.
Cisco Systems
Achieved Cisco Certified Network Associate certification, demonstrating expertise in network fundamentals and infrastructure.
IEEE Metaverse Conference 2022
Recognized for outstanding research contribution in metaverse technologies and virtual environment security.
Mohamed bin Zayed University of AI
Awarded graduate scholarship for exceptional performance in AI and machine learning studies at the world's first AI university.
DeepLearning.AI
Completed comprehensive deep learning specialization covering neural networks, CNN, RNN, and advanced architectures.
Technology Innovation Institute
Successfully completed advanced AI engineering internship, contributing to cutting-edge research and development projects.
Patent No. 20250008362
"Security in a Virtual Environment" - Innovation in virtual environment security protocols and authentication systems.
Stanford University
Completing prestigious machine learning specialization from world's leading AI research institution.
Developing, evaluating, and deploying Agentic AI models for real-world financial applications. Leading development of domain-specific Finance LLM evaluation frameworks while coordinating with academic and industry partners.
Optimized Falcon Model inference throughput by up to 22X using Pipeline parallelism, CUDA graph optimization, and kernel fusion. Built and deployed Falcon RAG system and Flutter mobile application for edge LLM deployment.
Designed comprehensive authentication frameworks spanning multiple technologies. Built integrated systems connecting mobile applications, blockchain, federated learning, and 3D environments. Won IEEE Best Paper Award at IEEE Metaverse-2022 Conference.
Applied data processing techniques including EDA, cleaning, normalization, and feature engineering. Used visualization techniques with PCA to transform raw data into actionable insights.
"Working with Daniel has been transformative for our AI initiatives. His deep understanding of distributed systems and edge deployment has accelerated our product development significantly."
"Daniel demonstrated exceptional technical skills and innovation during his time with us. His contributions to LLM optimization, RAG systems, and edge AI deployment were instrumental in advancing our research initiatives."
Ready to push the boundaries of what's possible with machine learning and AI? Let's collaborate on innovative projects that make a real impact.
📍 Location: Abu Dhabi, UAE
🏆 Status: UAE Golden Visa Holder
🎓 Currently: Applied Scientist at Inception G42