• Achieved a reduction in machine learning model training time by at least 30% by developing and deploying a containerised data science platform that enabled GPU usage within a JupyterHub environment. Further increased platform and resource efficiency through GPU virtualisation.• Performed automated infrastructure provisioning for one of the largest German telecommunication companies on AWS (Amazon Web Services). Saving approximately 4 hours every deployment and 100% error-free provisioning and deployment. Similar automation on GCP (Google Cloud Platform) for an American client.• Implemented a containerised machine learning model storage with seamless I/O operations on HDFS storage. Demonstrated the successful integration of MLFlow with Git and Data Version Control (DVC) to senior management.• Successful implementation of CI/CD GitLab pipelines for application deployment.• Collaborated with international development teams across three countries to incorporate product features into client deployments. Effective communication and time management was key for the successful delivery of features while working in an Agile development environment utilising JIRA.• Responsible for the creation of training plans, knowledge transfer sessions and mentoring of junior developers.• Resolution of vulnerabilities found in JFrog Xray scan results of developed Docker images to ensure integrity and resilience of deployed applications.• Technology Stack: Python, Docker, Kubernetes, JupyterHub, MLFlow, Terraform, AWS, GCP, DevOps, Machine Learning.

• Conducted comprehensive analysis to identify areas for revenue loss and provide recommendations for the improvement of a supermarket chain.

Learned the basics of Java and got familiarized with Android Studio

Utilised WordPress to develop websites.