Data Engineer with experience in building data pipelines, serverless web applications, and CI/CD automation using AWS, Python, SQL, Docker, and GitHub Actions. Also experienced in machine learning engineering, data proce…
Data Engineer with experience in building data pipelines, serverless web applications, and CI/CD automation using AWS, Python, SQL, Docker, and GitHub Actions. Also experienced in machine learning engineering, data processing, and integrating LLM tools such as OpenAI API and RAG (Langchain).
Developed a robust data pipeline for a job matching platform leveraging cloud tools and LLMs.
Optimized user engagement and increased average session length by 13%.
Authored a data-driven proposal detailing data pipeline design, cloud resources optimization, and cost analysts.
Maintained high quality of work through a rigorous review process.
Kept documentation up to date in Confluence.
Built a serverless web application using AWS Chalice framework.
Integrated AWS services such as S3, DynamoDB, and OpenSearch.
Emphasized seamless data integration and real-time processing to enhance functionality and user experience.
Implemented CI/CD pipeline with automated end-to-end testing and deployment using GitHub Actions, Pytest, and Docker.
Achieved over 95% test coverage.
Integrated Large Language Model tools such as OpenAI API and RAG (Langchain) for effective text processing in AI-driven job matching.
Monitored and enhanced data quality within the data pipeline by utilizing key performance metrics and statistical methods.
Developed a video understanding model for an online learning platform.
Incorporated a Temporal Shift Module, eye gaze detection, and behavior detection.
Resulted in a 15% increase in engagement prediction accuracy over traditional methods.
Orchestrated an automated data processing and hyperparameter tuning pipeline using Python and TensorFlow.
Leveraged AWS services such as S3 and EC2.
Resulted in a 10% improvement in model performance.
Developed a Flask-based backend for an Android app.
Served as the primary platform for product testing and demonstration.
Worked closely with the frontend team to achieve real-time attention level detection through efficient WLAN communication and robust API connectivity.
Developed a data processing pipeline in Python and TensorFlow for a luggage security model that identifies hazardous items in X-ray images.
Significantly reduced the development and tuning time for the model.
Established robust data quality assurance through testing algorithms and monitoring.
Implemented data augmentation techniques, including geometric transformations and color adjustments.
Applied transfer learning with pre-trained models like YOLO and ResNet.
Fine-tuned their last layers to significantly enhance the model's performance in the face of limited training data.
Explored anomaly detection using a Reconstruction-Based Convolutional Autoencoder.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
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