Data Engineer | Machine Learning Engineer | AI Engineer

I am a Junior Machine Learning and Data Science specialist with strong Python programming skills and hands-on experience in data analysis, data pipelines, and end-to-end model development. Over the past five years, I have gained valuable experience in process optimization, automation, and data-driven decision-making, transitioning from Quality Assurance to Data Engineering and Machine Learning roles. Currently, I am completing a Master’s degree in Data Science, focusing on scalable ML architectures, MLOps, and cloud-based solutions such as AWS and GCP to deliver measurable business impact and enhance user experience.

In my current role as a Technical Implementation Specialist at Brightfin, I design and maintain internal data pipelines to collect, transform, and monitor system performance metrics for enterprise clients, ensuring data quality and reliability. I have automated data quality checks and report generation using Python and SQL, which improved operational efficiency by approximately 25 percent. I collaborate closely with clients and developers to translate user needs into data-driven technical solutions and integrations, always focusing on improving the user experience.

Previously, I worked as a QA Engineer at TestTeamNow, where I analyzed large sets of test execution data to identify performance issues and optimize workflows across multiple projects. I built Python scripts for log analysis and result aggregation to uncover hidden trends and system bottlenecks, contributing to operational excellence. I also created and maintained structured datasets to improve reproducibility, traceability, and test reporting quality.

I have developed personal projects such as an MLOps pipeline prototype using Airflow, Docker, and CI/CD automation tools like GitHub Actions and ArgoCD. This project includes automated model retraining, evaluation, and deployment on AWS EC2, with plans to expand to AWS SageMaker and Terraform for infrastructure automation. Additionally, I have explored generative AI by experimenting with transformer models and text embeddings using Hugging Face and PyTorch, focusing on fine-tuning language models and ensuring reproducibility through containerization and experiment tracking.

My Master’s research focuses on the resilience and sustainability of e-commerce platforms under external shocks, investigating digital platforms, logistics, and consumer behavior. I synthesize academic and industry sources to highlight risk factors, mitigation strategies, and policy implications. I am actively enhancing my ML portfolio and contributing to open-source projects, and I am open to remote, hybrid, and on-site roles with relocation assistance within the EU.