POLITECNICO di Milano (September 2023 - Expected graduation in October 2025)
MSc in Computer Science and Engineering
POLITEHNICA University of Bucharest (October 2019 - July 2023)
BSc in Electronics, Telecommunications and Information Technology
GPA: 3.7
Relevant Coursework: Computer Programming, Data Structures & Algorithms, Databases, Object-Oriented Programming, Web Development, Neural Networks, Internet Programming Technologies, Software Engineering
Experience
DevOps Engineer at Nagarro (2022 - 2023)
Migrated CI/CD pipelines from GitLab to Azure DevOps by recreating pipeline stages, jobs, and environment-specific configurations in Azure Pipelines, ensuring compatibility and a smooth integration.
Maximized the efficiency of applications through implementation of containerization strategies using Docker, orchestration of containerized applications using Kubernetes, and cloud infrastructure management using AWS.
Trainer at Logiscool Bucharest (2022)
Taught children basic programming concepts using JavaScript and a block-based visual language.
Adapted teaching methods to accommodate different learning styles.
Personal Projects
Developed and hosted a personal resume website using Amazon S3 for static hosting, secured with CloudFront (HTTPS) and integrated a custom domain using CloudFlare
Built a fully serverless backend using API Gateway, AWS Lambda, and DynamoDB to implement a dynamic visitor counter that tracks website visits
Automated full CI/CD pipelines with GitHub Actions, enabling automatic deployments of both backend and frontend from version-controlled repositories using IaC (Terraform)
Architected and deployed a cloud infrastructure using Terraform modules, including VPC, subnets, internet gateway, NAT gateway, and custom routing tables.
Set up public and private virtual servers, utilizing the public server to port forward website traffic from the client to the private server, allowing the private server to remain isolated and secure.
Integrated a database to store website data and streamline data flow between the website and the backend.
Managed to choose a suitable residual neural network model and a dataset, containing over 100,000 images of 771 snake species from all over the world.
Successfully trained the chosen neural network on the dataset, obtaining a 78.53% accuracy, a 1.84% training loss, and an 84% validation accuracy.
Developed a user-friendly Android application that integrated a trained neural network model, enabling users to select any image, process it through the model, and receive a prediction.