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My career spans research, data engineering, and cloud infrastructure. I have managed production servers and ~30 live deployments, built CI/CD automation with GitHub Actions, Terraform, and Ansible, and conducted applied research on AI-driven forecasting. Whether designing scalable ML architectures or resolving infrastructure issues for a team of 15+ engineers, my experience blends hands-on engineering with rigorous data-driven problem-solving.
Manage and maintain 4 production servers (AWS, VPS, Hetzner Cloud, Cloudflare R2) with end-to-end deployment and maintenance of ~30 active projects. Built CI/CD pipelines with GitHub Actions, GitLab CI/CD, Jenkins, Terraform and Ansible, cutting deployment time by 80%. Serve as primary technical contact for a team of 15–17 engineers, handling Nginx/Apache, Docker, PostgreSQL, DNS, SSL, network security, and disaster recovery.
Conducted applied research on AI-driven forecasting and automation models, collaborated on scalable machine learning architectures and predictive analytics tools, and prepared research manuscripts using Python, PyTorch, and Azure ML.
Managed cross-functional tasks across data science, data engineering, analytics, and technical support. Built and maintained data pipelines, performed data analysis and preprocessing, supported machine learning workflows, and prepared analytical reports for decision-making.
Developed data engineering solutions supporting AI and predictive analytics initiatives. Implemented data validation frameworks achieving 99% data quality, and enabled real-time data streaming and model training in Databricks.
Delivered 60+ analytical projects for global clients covering data cleaning, visualization, and forecasting. Designed custom ML-based dashboards with Python, SQL, and Power BI, and supported startups and researchers in data preparation for publications and prototypes.