Rafid Sorker

• Developed AI-powered software solutions optimizing embedded systems and computing pipelines with Python and Java, improving efficiency by 40%.
• Contributed to Generative AI for Electronics, enhancing real-time computing performance and automation in global open-source projects.
• Designed and implemented RESTful APIs, improving back-end efficiency and real-time data processing with a 30% performance boost.
• Built HF signal processing workflows using GNU Radio, Flask, and CesiumJS, enabling real-time RF coverage visualization.
• Delivering a stock risk visualization tool with Java and a machine learning-based risk analysis framework in Python.
• Collaborated with a global team to create AI-driven solutions in healthcare, green energy, and embedded automation.

Citi ICG Technology Software Development Job Simulation on Forage - December 2024

• Completed a job simulation involving hypothetical tasks to improve Citi's loan management system and stock market risk reporting
• Created a state diagram of the loan management process using the Unified Modelling Language (UML)
• Researched potential machine learning systems to assess credit risk and provided recommendations for next steps
• Used Java to build an internal tool visualising stock market risk in real time

Software Developer, 02/2025 - present

OpenQQuantify - remote

• Contributing to open-source projects in Generative AI for Electronics, Embedded Systems, and Integrated Software Pipelines.
• Collaborating with a global team to build cutting-edge solutions that push the boundaries of open computing and AI-driven automation.
• Developing and optimizing back-end systems, implementing Object-Oriented Programming (OOP) principles, and improving software performance.
• Actively working on HF signal processing, GNU Radio flowgraphs, Flask APIs, and CesiumJS for real-time visualization and analysis.
• Engaging in research and innovation to drive impactful advancements in healthcare, green energy, and agriculture using AI and embedded systems.

• Developed a Java-based memory game for SwiftBot, mapping buttons (A, B, X, Y) to LED colors (Red, Green, Blue, Yellow).
• Designed an algorithm to track random color sequences, validate user input, and increase difficulty each round.
• Implemented scoring logic and game-over conditions, triggering a ‘V’-shaped celebration dive for scores ≥5.
• Built a command-line interface (CLI) for user interaction, integrating SwiftBot movement and real-time feedback.