AI Engineer · Machine Learning Engineer · Generative AI Specialist · Kaggle Master (Top 1%) · MSc in Data Science
I design and deploy generative AI systems, LLM pipelines, RAG search, Whisper ASR, and multimodal vision — with a strong focus on inference efficiency, scalable MLOps, and production-ready delivery on AWS and Kubernetes.
About
I work across the full AI delivery stack: data pipelines, experimentation, model fine-tuning, inference optimization, deployment, and monitoring. My recent work spans generative AI systems, enterprise chatbots, speech-to-text pipelines, semantic search, video intelligence, and production recommendation engines.
The focus is consistent: ship systems that are reliable, cost-aware, and tied to business outcomes rather than demos. That includes LoRA/QLoRA fine-tuning of open-weight LLMs, RAG pipelines with FAISS and Pinecone, real-time inference, and ML platform practices including CI/CD, MLflow, and experiment tracking.
Deep learning work is built on PyTorch — from transformer architectures to distributed training across multi-GPU setups. Applied to fine-tuning LLaMA and Mistral models, building vision encoders, and optimizing models for production inference with quantization and dynamic batching.
On the infrastructure side: Docker, Kubernetes, AWS, and GCP Vertex AI for ML workload orchestration, with automated monitoring and retraining pipelines. Machine learning system design — from data versioning through model registry to serving — is as much the job as the models themselves.
Skills
Projects
Built enterprise-grade speech-to-text, LLM applications, and chatbot systems for large-scale client delivery.
Impact: moved AI initiatives from exploration into production-focused delivery pipelines.
Engineered meeting agents that transcribe conversations, generate summaries, and automate follow-up workflows using a Whisper + LLM pipeline.
Impact: reduced manual documentation work and accelerated meeting turnaround.
Implemented semantic resume-to-role matching with retrieval-augmented search to improve candidate ranking quality in recruitment workflows.
Impact: increased candidate-job fit accuracy for hiring teams.
Developed search, recommendation, auto-tagging, and image retrieval systems for fashion e-commerce using CV and embedding-based retrieval.
Impact: improved engagement and streamlined catalog operations.
AI-powered FPL Scout using historical and live data with ML models to predict player performance and optimize team selection.
Impact: enables data-driven FPL decisions with real-time predictive insights.
Contributed to the LangChain MCP adapter ecosystem and published reproducible Kaggle notebooks focused on practical, educational ML workflows.
Impact: extended open-source tooling and community learning resources.
Services
One-on-one technical consultation to scope AI opportunities, architecture decisions, and practical implementation plans.
Implementation and delivery of production-ready ML/DL systems from data preparation through modeling and deployment.
Design and development of custom LLM-powered agents tailored to automate workflows and support domain-specific tasks.
End-to-end computer vision solutions including detection, segmentation, and video analysis pipelines.
Credentials
Cairo University · Jan 2024 to Present
Port-Said University · Sept 2016 to Jul 2021
Top 1% Kaggle Code Master, 2 silver medals, 2 bronze medals, Upwork Top-Rated freelancer, 100% Job Success Score.
Blog
A ground-up explanation of how Large Language Models work, covering transformer architecture, pretraining, fine-tuning, and the intuition behind coherent text generation.
February 2026 · Large Language Models · Generative AI
Discover how machine learning models, time-series analysis, and live football data APIs can be combined to automate FPL decision-making and improve player performance predictions.
August 2025 · Machine Learning · Predictive Analytics
Contact
If you need LLM systems, semantic retrieval, speech pipelines, or ML platform work shipped with production discipline, reach out directly.