AI/Machine Learning Technical Leader

7/2023 – 12/2024

About the Projects: Led AI-driven healthcare projects focused on diagnostic and therapeutic advancements, including breast cancer detection, telemedicine solutions, virtual reality healthcare chatbots, and sign language translators.

Challenge: Develop accurate and scalable AI models for healthcare applications, ensuring high performance in diagnostic tools and seamless integration with existing systems.

Solution:
Breast Cancer Detection: Implemented image classification and segmentation models for accurate detection and analysis of cancerous tissues.
Telemedicine and Healthcare Assistants: Developed AI tools for remote consultations and patient support, enhancing diagnostic accuracy and care.
Virtual Reality and Chatbot Solutions: Created immersive environments and AI-powered chatbots to improve patient interaction and support.
Sign Language and Data Extraction: Built tools to enhance communication with hearing-impaired patients and automate patient data processing.

Projektentwickler

12/2019 – 6/2023

About the Projects: Worked on various computer vision projects focused on real-time video processing at the edge, tackling complex challenges in motion detection, anomaly detection, activity recognition, object detection, and object classification.

Challenge: Develop and deploy highly efficient computer vision models capable of processing video data in real-time on edge devices, ensuring minimal latency and high accuracy in diverse environmental conditions.

Solution:
Motion and Anomaly Detection: Designed and implemented models to detect unusual activities and motion patterns in real-time, optimizing algorithms for edge devices.
Activity Recognition and Object Detection: Developed systems to accurately recognize and classify human activities and objects from live video feeds, ensuring quick response times and reliable performance.
Real-Time Edge Processing: Leveraged Python, PyTorch, and OpenCV to create efficient pipelines for processing video data directly on edge devices, minimizing the need for cloud resources and enhancing data privacy.

Responsibilities:
Developed and optimized computer vision models for real-time video processing on edge devices.
Implemented robust data processing pipelines using Python and PyTorch, focusing on low latency and high accuracy.
Collaborated with cross-functional teams to integrate models into production systems, ensuring seamless deployment and operation.
Maintained code quality and version control through Git, and automated testing and deployment using Jenkins.

Projektentwickler

7/2017 – 3/2019

About the Project: Streamlined data management and reporting processes for a media streaming company by migrating their data infrastructure to the cloud and automating their reporting workflows.

Challenge: The company faced challenges with its legacy data systems, which were inefficient and costly to maintain, and required a modern solution to automate reporting and improve decision-making capabilities.

Solution:
Successfully migrated the company’s data infrastructure to a cloud-based platform, ensuring scalability, security, and cost efficiency.
Automated reporting processes by integrating a custom-built reporting engine, which consolidated data from multiple sources and generated real-time insights through user-friendly dashboards.
Implemented a cloud data warehouse and ETL pipelines to manage and centralize data from various sources, reducing manual intervention and improving data accuracy.
Developed a scalable solution that reduced the monthly cost of ownership by 60% and eliminated the need for manual report generation.

Responsibilities:
Led the cloud migration process, focusing on data integration and scalability.
Developed automated reporting solutions using Tableau and other BI tools.
Optimized ETL processes to ensure efficient data flow and storage.
Collaborated with cross-functional teams to ensure the solution met the company’s evolving needs.

Projektentwickler