• Lead the end-to-end process of computer vision object detection model development, including data preprocessing, feature engineering, training, fine-tuning, and production deployment.• Enhance real-time processing speed by optimizing post-processing workflows for AI object detection on camera feeds, significantly increasing frames per second (FPS) and overall system efficiency.• Create and deploy a comprehensive data communication infrastructure using the Kafka messaging system to facilitate the smooth transmission of flight information and data generated by AI models.• Design and implement an audio classification model using spectrograms and CNNs, managing end-to-end processes from dataset creation to backend integration, enhancing system capabilities.• Overhaul backend service logic for AI model integration, implementing targeted optimizations to reduce database load from ~40 requests per second to 1, enhancing system efficiency and responsiveness.• Implement best practices for software development by introducing version control and establishing robust debugging and testing procedures, including the creation of test beds, enhancing overall system reliability and code quality.• Lead and mentor a team of annotators, providing guidance on meticulous data annotation practices, resulting in high-quality datasets instrumental in designing and training effective object detection models, contributing to the success of AI initiatives.

• Create and maintain automated testing suites for APIs and backend technologies, ensuring high quality and efficiency of software releases.• Design and develop custom virtual devices that replicated real-world devices with high fidelity, enabling accurate and efficient software testing and debugging.• Conduct stress testing and analyze data to identify and address performance bottlenecks, resulting in significant improvements to system reliability and response time.• Conducted code reviews and collaborated with development teams to identify and address technical issues.

• Automated nuclear plant operations by using computer vision and machine learning techniques.• Developed a deep learning model with the state-of-the-art Yolo architecture using Tensorflow to perform object detection with convolutional neural networks on images.• Developed various CNNs for key points detection using U-Net architecture.• Implemented different CNN architectures for image classification problems.• Created, sanitized, and augmented real and synthetic image datasets.

• Analyze memory dumps for anomaly detection. • Identify and implement malware detection algorithms for malicious software.• Extract and analyze kernel level data structures from live virtual machines.• Develop multithreading, multitasking, thread synchronized and parallel programming software. • Deploy and manage type 1 hypervisors ( Xen | KVM ).• Deploy and manage virtual machines in a controlled environment.

• Analyze complex systems, resolve resources leaks and deadlocks, documenting of code, re-program bad programming patterns, fix bottlenecks, monitor memory usage, identify and repair miss-use of APIs.• Isolate cause of failure from log analysis using specialized monitoring tools such as Dynatrace, Splunk as well as command line.• Perform verbal and written descriptions of the environment and applications failures. • Provide and implement course of action for application failures and misbehaviors.

School of Computing and Information Sciences at FIU August 2016 — Present• Courteously greet a versatile of students in person and on phone, provide information and assistance regarding Computer Science/Information Technology program.• Support students and staff with technical issues regarding computer/network maintenance.• Maintain log books, including sign-in/out logs and calls received.