Integrated complex AI-based energy system models into the frontend framework, enabling visualization of insights for 10+ key clients and maximizing energy utilization. Architected seamless data flow between AI models and the user interface for rapid, actionable reporting to maximize energy efficiency.

DevOps & Machine Learning Intern at Deeptronics GmbH

February 1, 2025 - February 1, 2025

Transformed high-rate datasets via data preprocessing and feature engineering to accelerate ML model deployment by 135%. Implemented robust CI/CD pipelines with Jenkins, GitLab CI, and Azure Data Factory, reducing model deployment time by 40%. Ensured GDPR compliance by integrating security measures across pipelines.

Student Research Assistant in Machine Learning and AI at Chair of Factory Automation and Production Systems, Nürnberg, Germany

October 1, 2024 - October 1, 2024

Deployed scalable FastAPI-based ML services on Azure AKS, reducing prediction latency by 60% and integrating Explainable AI (XAI) for model transparency. Streamlined MLOps workflows by integrating ML models with Azure Machine Learning for automated continuous delivery. Implemented a robust monitoring stack with Prometheus and Grafana and developed dashboards with Power BI and Dash for real-time insights to support A/B testing.

Master’s Thesis – Anomaly Detection in Time Series using ML/DL at AVL List GmbH

June 1, 2024 - June 1, 2024

Developed ETL pipelines with advanced preprocessing to address class imbalance, improving model training accuracy to 95%. Fine-tuned high-performance multi-output ML/DL models using PyTorch and TensorFlow, achieving 98.7% accuracy and enabling real-time predictions via Simulink integration.

Junior Data Scientist at Decision Point Analytics

March 1, 2021 - March 1, 2021

Collaborated with FMCG clients to analyze data requirements and develop data-driven solutions. Optimized ETL pipelines using Python, Apache Spark, and SQL to streamline data flow from large datasets across multiple cloud storages into AI-driven systems. Applied regression, decision trees, and ensemble models in Azure ML, improving product adoption by 90%. Automated data validation and quality checks within ETL processes for SQL and NoSQL sources.