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AI Engineer with 4+ years in Machine Learning & Data Science
Results-oriented Data Scientist with 4+ years of hands-on experience in designing and deploying machine learning and deep learning solutions across forecasting, computer vision, and NLP domains. Holds a master's degree in information technology from the University of Stuttgart. Proven ability to automate data pipelines, extract actionable insights from large, complex datasets, and communicate findings effectively to both technical and non-technical stakeholders. Adept at using tools like Python, SQL, TensorFlow, PyTorch, and Scikit-learn to drive data-informed decision-making. Passionate about solving real-world problems through collaborative innovation and continuous learning.
University of Stuttgart
Master of Science · Information Technology
August 1, 2019 – June 30, 2022
YMCA University of Science and Technology
Bachelor of Engineering (BE) · Information Technology
August 1, 2015 – June 30, 2019
K2 Systems GmbH
Data Scientist
December 1, 2022 – Present
Renningen, Baden-Württemberg, Germany
Storeserver Systems GmbH
Machine Learning Intern
May 1, 2021 – October 1, 2021
Stuttgart, Baden-Württemberg, Germany
Storeserver Systems GmbH
Data Science & Machine Learning Engineer (Working Student)
January 1, 2021 – April 1, 2022
Stuttgart, Baden-Württemberg, Germany
Navitus
Android Developer Intern
January 1, 2019 – June 1, 2019
New Delhi, Delhi, India
Generating TEI-based XML for literary texts
December 1, 2021 – July 1, 2022
Applied Masked Language Modeling (MLM) techniques to predict XML tags from unstructured text, enhancing automated document structuring and metadata generation. Evaluated and compared the performance of BERT, ROBERTa, and XLNet models across diverse datasets. Identified BERT as the most effective model, achieving a 15% increase in precision for tag prediction tasks, leading to improved model reliability and downstream processing accuracy.
Automatic Segmentation of German Dramatic Texts
December 1, 2020 – May 1, 2021
Tackled the challenge of enriching plain text with learned XML elements, enhancing document structure and semantic clarity. Fine-tuned BERT for XML tag prediction tasks, adapting the model to domain-specific data and improving contextual understanding. Successfully generated structured XML files from model predictions, automating metadata creation and improving downstream data usability. Authored and published a comprehensive research paper ("https://ceur-ws.org/Vol-2989/short_paper34.pdf") that analysed automatic segmentation techniques, contributing to the academic community
Smart Diner System
May 1, 2020 – August 1, 2020
Developed a smart dining automation system using sensor integration and Android applications to streamline food ordering and service management. Created two Android apps: one for customers to place orders, and another for managers and waitstaff to manage and track orders in real time. Utilized Firebase for cloud-based data storage and retrieval, enabling seamless synchronization across devices and users. Successfully Contributed to improving operational efficiency and customer experience through mobile-driven automation.
Credit Card Fraud Detection
September 1, 2018 – November 1, 2018
Implemented a Support Vector Machine (SVM) model to detect fraudulent credit card transactions using real-world imbalanced datasets. Conducted a comparative analysis of SVM vs. other classification algorithms (e.g., Logistic Regression, Random Forest), evaluating performance across precision, recall, and F1-score. Achieved 96-98% accuracy, significantly improving fraud detection capabilities and reducing false positives.
A comprehensive research paper
Unknown
January 1, 2021 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from academic NLP research to practical fraud detection and smart dining systems, indicates a broad interest and ability to apply AI/ML in various domains. Their experience in both Germany and India, coupled with explicit mention of adaptability and team-working skills, suggests a strong cultural fit for diverse and international teams. The publication of a research paper also shows a commitment to contributing to the academic community and continuous learning.
Soft Skills & Operational Fit
The candidate demonstrates strong team collaboration skills, having worked in Agile cross-functional teams and diverse environments. Their experience in communicating complex technical findings to non-technical stakeholders indicates good communication and presentation abilities. The resume also highlights adaptability, having worked in different cultures across two continents. These attributes suggest a good operational fit for collaborative and dynamic AI engineering teams.