AI Engineer

1/2024 – offen

Designed system patterns for 3 distinct scenarios for a P2P Energy Trading application leveraging Blockchain technology. Implemented the application using Hyperledger Fabric v2.x Blockchain and the Smart Contracts using Golang. Tested the Fabric Network and transactions of the application within the Ubunto environment and deployed Fabric Network

Blockchain (allg.), Data Science

3/2023 – 10/2023

Analysed Raw data involving feature extraction and data cleaning processes using Pandas and Seaborn libraries. Designed a Deep Reinforcement Learning system to enhance the prediction of battery state variables such as the voltage. Implemented using PyTorch 2.1 a battery simulation model and worked on its optimization using Deep Reinforcement Learning techniques to determine the optimal configuration of the battery parameters that enhances the model accuracy. Optimized the training outcomes of the DQN agent through fine-tuning the neural net hyperparameters and incorporating a secondary Deep Reinforcement Learning algorithm, namely Dueling Deep Q-Networks. Tested the trained DQN agents and validated the proposed method based on real-time series data ending up outperforming the existing prediction methods by \textbf{10\%} and wrote a detailed journal outlining the conceptual framework and methodology which earned the distinction of being published in MDPI Journal.

Data Science, Python

12/2021 – 12/2022

Build a prototype of an autonomous robot from scratch: Learning and using new ADAS technologies to achieve precise and accurate positioning namely Radar, LiDar, and Depth Camera. Established a comprehensive dashboard for an end-to-end application facilitating the real-time monitoring of the autonomous robot, enabling it to navigate specific obstacles with precision and efficiency

C++