Senior Data & ML Engineer specializing in building production-grade data platforms and machine learning systems. I design end-to-end architectures—from raw data ingestion to real-time analytics and deployed ML models— with a strong focus on scalability, reliability, and cost efficiency across AWS and Azure. My work bridges data engineering, MLOps, and applied machine learning to turn complex systems into measurable business impact.
I build data systems that are designed for real production constraints—scale, latency, cost, and maintainability. My experience spans large-scale batch processing, real-time streaming, and machine learning pipelines that support critical business decisions. I focus on architectures that teams can operate, extend, and trust.
Alongside hands-on engineering, I bring a strong teaching and leadership background. I have designed curricula, led technical teams, and mentored engineers working on real production systems. This combination allows me to communicate clearly with stakeholders while delivering robust, enterprise-ready solutions without unnecessary complexity.
Data Engineering, ML Engineering, MLOps, Cloud-Native Systems
Giza, Egypt | Remote-First | Global Time Zones
Scalable Data Platforms, Production ML, and Engineer Enablement
Production-proven expertise across the modern data stack
Build scalable data pipelines processing TB+ daily using Spark, Kafka, and Airflow on AWS/Azure infrastructure
Design and deploy production-grade solutions across AWS and Azure with IaC, cost optimization, and high availability
Deploy production ML systems with CI/CD, monitoring, and scalable inference pipelines using MLflow, Kubeflow, and cloud-native tools
Production deployments driving real business impact
Leading the design and delivery of production-grade machine learning and data systems. Responsible for architecture decisions, model deployment strategies, and operational reliability. I mentor engineers on building maintainable ML pipelines and cloud-native data platforms used in real production environments.
-->Designed and delivered production-focused training programs in data engineering, machine learning, and cloud computing. Emphasis on real-world architectures, deployment workflows, and operational tradeoffs. Helped engineers transition from academic or notebook-level work to production-ready systems.
Built enterprise data infrastructure processing 2TB+ daily across AWS and Azure. Deployed ML-powered churn prediction reducing customer attrition by 35%, saving $1.2M annually.
-->Developed scalable IoT data platform using AWS IoT Core, processing 1M+ sensor readings daily. Published research at IEEE Conference on cloud-based ML optimization achieving 40% latency reduction.
Delivered advanced training on integrating embedded systems with cloud infrastructure. Developed serverless IoT architecture curriculum adopted by national technical education program.
Proven expertise through rigorous industry certification
Advanced ML deployment, model optimization, and production ML systems on AWS
Enterprise data lakes, ETL pipelines, and analytics solutions architecture
Advanced neural networks, CNNs, RNNs, and production deep learning systems
Distributed computing, Hadoop ecosystem, and big data architecture
Tools and technologies I use to deliver enterprise solutions
Real results from real projects
"Ahmed built our entire data infrastructure from scratch. His ML models increased our conversion rate by 42%. Highly recommend for any serious data project."
"Exceptional engineer! Delivered our real-time analytics platform ahead of schedule. His expertise in Kafka and Spark is world-class."
"Best data engineer we've worked with. Reduced our AWS costs by 50% while improving performance. A rare combination of skills."
If you need robust data infrastructure, reliable ML systems, or technical leadership for complex projects, I’m available for consulting, long-term engagements, and selective full-time opportunities.
