rafay T.
Junior AI Engineer
Open to offersNew to PlatformAbout
Abdul R. is a highly skilled AI Engineer specializing in Computer Vision, Large Language Models (LLMs), and real-time AI systems. Based in Karachi, Pakistan, he has demonstrated expertise in building production-grade intelligent applications. Currently serving as an AI Engineer at Oval, Abdul has developed real-time AI systems tailored for smart environments, including applications for intelligent monitoring and automation. His work involves creating deep learning-based video analytics pipelines for eavesdropper detection and fall prevention, and he has contributed to the optimization of inference pipelines for improved latency. Previously, he worked as an AI Trainee at QBS CO, where he focused on enterprise-level video analytics, leveraging technologies such as YOLO for detection and deep learning models for enhanced prediction stability. Abdul's project portfolio includes developing AI-powered retail video analytics and audio sentiment classification systems, showcasing his deep understanding and application of advanced AI technologies. He holds a BS in Computer Science from the National University of Computer and Emerging Sciences (FAST), Karachi.
Experience
AI Engineer
Oval · 2026 — PresentCreated and implemented real-time AI systems for smart environments, focusing on intelligent monitoring and event-driven alerting. Developed computer vision solutions for various detection tasks using deep learning-based video analytics pipelines. Enhanced real-time inference pipelines with tracking and temporal analysis to achieve low-latency performance. Built voice-controlled AI agents for natural language interactions with smart devices. Integrated AI models with backend services and IoT systems for smooth real-time communication.AI Trainee (Computer Vision)
QBS CO · 2025 — 2026Constructed real-time computer vision pipelines for enterprise video analytics utilizing YOLO-based detection and deep learning methodologies. Developed multi-model vision systems that encompassed detection, segmentation, pose estimation, and attribute classification. Applied object tracking and temporal smoothing techniques to enhance prediction consistency across video streams. Improved inference workflows for low-latency deployment suitable for scalable AI applications. Worked on integrating computer vision services with FastAPI and assessed real-time communication systems including MQTT, NATS, and WebSockets.
Skills & Expertise
Education
- Bachelor of Science in Computer ScienceNational University of Computer and Emerging Sciences (FAST) · 2021 — 2025
Interested in this professional?
Sign in as an employer to save this profile or invite rafay T. to a job.
Sign in as an employer