Staff AI Software Engineer @ Qualcomm | AI Inference Acceleration | Generative AI | System ML/AI | Embedded Systems | ISO-26262 | Deep Learning
Proficient in cutting-edge AI and Generative AI technologies, working on Qualcomm AI Runtime Engine and AI Accelerator HW.
Transformer models (GPT/LLaMA), Deep Learning Frameworks
Experienced in Embedded Systems, Automotive Perception Stack, ADAS solutions, and safety-critical systems development.
ISO-26262 Certified | Real-Time Systems Expert
MS in Computer Engineering from University of Waterloo. Published researcher in embedded systems and autonomous driving safety.
7+ Publications | Teaching Experience
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Qualcomm · Dec 2024 – Present
Leading Qualcomm AI Runtime Engine and QAIRT features for accelerator hardware with AUTOSAR C++ compliant SDK.
View details →Qualcomm · Aug 2022 – Nov 2024
Developed Qualcomm AI Engine Direct SDK and ML operators, enabling efficient deployment on Qualcomm AI accelerator hardware.
View details →Seneca College · May 2022 – Aug 2022
Taught programming courses in C++, Python, and ROS, bridging industry experience with applied engineering education.
View details →LeddarTech · Jun 2019 – Aug 2022
Built ADAS perception software and deep learning pipelines for object detection from point cloud data and camera sensors.
View details →Ciena · Feb 2018 – May 2019
Developed embedded middleware and debugging frameworks for Ciena 6500 packet-optical platforms on Linux.
View details →Ricardo (Peloton Technology) · May 2017 – Dec 2017
Executed ISO‑26262 safety activities including FMEA and FTA for autonomous truck platooning and automotive systems.
View details →University of Waterloo · Sep 2016 – Aug 2017
Supported courses in operating systems and embedded systems, including lab design and a small real-time executive (RTX).
View details →Magna Electronics · 2015 – 2017
Worked on parking slot detection and camera module safety for Magna Auto Parking systems using C/C++ and computer vision.
View details →
Development of AI Runtime Engine for Qualcomm's AI Accelerator Hardware. Feature development, optimization, and AUTOSAR C++ compliance for QNN SDK.
Working with state-of-the-art transformer models including GPT and LLaMA architectures. Deep learning frameworks and ML systems optimization.
Deep learning for pedestrian, car, and object detection from point cloud data. Computer vision applications for autonomous driving systems.
Machine learning operators development and ML testing infrastructure. Continuous integration and deployment pipelines for AI systems.
AI development on Snapdragon System-on-Chips. Optimizing neural networks for mobile and embedded AI accelerators.
Advanced deep learning with Convolutional and Long Short-Term Memory networks. TensorFlow, Keras, and PyTorch implementations.
Master of Science (MS), Computer Engineering
Specialized in Real-Time Embedded Systems and Software. Worked on specification-based bug detection for embedded software and machine learning projects. Published papers in embedded systems and safety analysis for autonomous driving.
Bachelor of Engineering (B.E.), Electrical & Electronics Engineering
Worked on command and data handling for Project STUDSAT-2. Developed a real-time operating system using FreeRTOS for satellite operations.
ISO-26262 Functional Safety & Full Stack Development
ISO-26262 Automotive Functional Safety certified. Full Stack Web Developer Certification from Free Code Camp.
Advanced debugging techniques for embedded systems at deployment stage, enabling real-time monitoring without standard debugging tools.
Implemented Real-Time Operating System using FreeRTOS with 64 uniquely identified tasks for twin nano-satellite operations.
Designed, simulated and tested four wideband antennas for aircraft applications including angle of arrival estimation and adaptive nulling.
IEEE International Conference on Metrology for Aerospace, 2015
IEEE International Conference on Metrology for Aerospace, 2015
66th International Astronautical Congress, 2015
International Conference on Academic Research in Engineering, Science And Technology, 2015
International Journal of Computer Applications 97(11):13-18, July 2014
International Journal of Research in Engineering & Technology (IMPACT: IJRET), Vol. 2, Issue 3, Mar 2014
International Journal of Electronics Engineering, ISSN: 0973-7383, No.5 (2013) Issue No.: 2 (2013)
2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, Germany, 2018, pp. 1147-1150, doi: 10.23919/DATE.2018.8342184
Exploring the evolution of generative AI from large language models to sophisticated multimodal systems, covering transformer architectures, inference optimization, and ethical considerations.
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Learn about model compression techniques, efficient architectures, runtime optimization, and hardware acceleration strategies that enable sophisticated AI on smartphones, IoT devices, and embedded systems.
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A comprehensive exploration of transformer architecture, from self-attention mechanisms to modern variants like GPT and BERT. Covers training techniques, efficiency improvements, and applications beyond NLP.
Read Full ArticleSeneca College
"As an adjunct professor, Kamal demonstrated exceptional teaching abilities and deep technical knowledge. His students consistently praised his clear explanations of complex concepts and his dedication to their success. He brings real-world industry experience into the classroom."
University of Waterloo
"Kamal's guidance as a teaching assistant was invaluable. His expertise in embedded systems and real-time operating systems helped me understand complex concepts. He's patient, knowledgeable, and always willing to help students succeed."
LeddarTech
"Working with Kamal on ADAS solutions was a great experience. His expertise in computer vision and deep learning, combined with his strong C++ skills, made him an invaluable team member. He consistently delivered high-quality, production-ready code."