I’m an ML & MLOps Engineer at Wevioo, with almost 2 years of experience building and shipping machine learning models end-to-end. From model development to automated deployment and monitoring, I cover the full ML lifecycle.
What I do:
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Build scalable models (classification, regression, recommendation)
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Deploy with tools like Kubeflow, SageMaker, Docker
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Set up monitoring with Prometheus & Grafana
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Work across AWS, Azure, Terraform, and CI/CD pipelines
- Have strong expertise in Python ( FastAPI )
- Can develop frontend apps using Streamlit framework ( python) or Angular
Highlights:
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Delivered a contextual bandit recommender for BMW Group
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Built robust monitoring and alerting for real-time ML services
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Created fast, reliable pipelines for diverse ML endpoints
If you need help from prototype to production, debugging models, or scaling your ML infrastructure, let’s connect!
Here you can find my LinkedIn:
https://www.linkedin.com/in/arfaoui-arij-3636201ba/
Here you can find a comple MLops project I worked on:
https://github.com/areejarfaoui18/MLops
here’s the summary of the project: This project showcases a complete MLOps pipeline that takes a machine learning model from training to production using modern DevOps and cloud-native practices. It includes model versioning with MLflow, serving via FastAPI, containerization with Docker, automated CI/CD using GitHub Actions, and cloud deployment on Azure Container Apps. The system is stress-tested with Locust and monitored with Prometheus and Grafana, including alerting via email. This setup ensures scalable, observable, and automated machine learning deployment, suitable for real-world production environments.
Ariana