I am a Computer Science MSc graduate and engineering degree holder with experience in software engineering, data science, cloud technologies, and applied deep learning research.
I have developed machine-learning forecasting solutions for cloud infrastructure costs, from comparing models and evaluating performance to integrating selected models into production pipelines.
I have hands-on experience building REST APIs and data-driven applications using Python, JavaScript, SQL, Flask, and cloud services such as AWS, GCP, and BigQuery.
My work has included FinOps-oriented cloud analytics, including collecting and analyzing AWS CloudWatch metrics to identify underused EC2 resources and support infrastructure cost optimization.
I also have research experience in deep learning for healthcare audio classification, using transfer learning, data augmentation, TensorFlow, Keras, and MobileNet architectures.
I am available immediately and looking for an opportunity to contribute across the complete project lifecycle, from problem definition and development through testing and deployment.
Developed a machine learning forecasting model to estimate future cloud infrastructure costs from historical data for FinOps decision-making.
Compared multiple modeling approaches and integrated the selected solution into a production pipeline, achieving SMAPE below 15%. Designed and deployed a REST API to serve the forecasting solution through an interactive user interface.
Built a proof of concept to collect and analyze Amazon CloudWatch metrics for EC2 instances.
Enabled the FinOps team to identify underused cloud resources, provide configuration recommendations to developers, maintain application performance, and optimize cloud infrastructure costs.
Diplôme d’Ingénieur, Software Development, Big Data & Cloud
Engineering degree in Software Development, Big Data and Cloud.
Master of Science, Computer Science
Master of Science in Computer Science with a CGPA of 3.69/4.0. Thesis focused on classification of abnormal respiratory sounds using deep learning.
Bachelor of Science, Computer Science
Bachelor of Science in Computer Science with an ICGPA of 3.54/4.0.
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