Awais Nazir

PROFESSIONAL PROFILE Machine Learning Engineer and final-year Software Engineering student at NUST, with strong industry and research experience in artificial intelligence, computer vision, and scalable machine learning systems. Experienced in building production-ready, low- latency ML pipelines and deploying end-to-end AI solutions in real-world environments. Technical Expertise: PyTorch, TensorFlow, Transformers, GANs, Computer Vision, NLP, Vector Databases MLOps & Cloud: AWS (Lambda, SQS, EC2, API Gateway, ALB, ASG), Docker, CI/CD, GPU acceleration (TensorRT) Systems & Performance: Multithreaded pipelines, queue-driven architectures, parallel CPU/GPU execution, inference optimization Applied Research: Autonomous AI agents, adaptive web interaction, reinforcement learning, temporal event detection Proven ability to bridge research and production, having reduced inference latency by ~40% in deployed systems and delivered scalable ML solutions across cloud-native architectures. Actively researching autonomous AI agents and adaptive web interaction, with a strong passion for applying cutting-edge AI research to solve complex, real-world problems with measurable impact. EDUCATION BESE (Bachelors Of Software Engineering) School of Electrical Engineering and Computer Sciences (SEECS) , Islamabad , 3.65 (2026) INTERNSHIP EXPERIENCE Pineamite Limited 01-Dec-2024 - 09-Jan-2026 1. Designed and optimized multithreaded, queue-driven ML pipelines enabling parallel CPU and GPU execution, achieving ~40% reduction in end-to-end inference latency. 2. Built and deployed scalable, cloud-native ML workflows on AWS for UK rally car racing telemetry using Lambda, SQS, API Gateway, EC2, ALB, ASG, and containerized services. 3. Developed a temporal event-detection model using Transformer Encoder architecture to accurately localize critical racing events from time-series data. 4. Implemented GPU acceleration and inference optimization using TensorRT to improve performance in production environments. 5. Applied MLOps best practices including version control, containerization, automated deployment, and monitoring. Made IT 01-Jun-2024 - 31-Aug-2024 1. Developed an AI-powered semantic search platform supporting text-based, image-based, and hybrid queries. 2. Utilized OpenAI CLIP embeddings and Milvus vector database for efficient multimodal retrieval. 3. Designed similarity scoring using weighted fusion of text and image embeddings to improve search relevance. 4. Conducted experiments and evaluations to validate retrieval accuracy and system performance. 5. Developed a facial recognition system using Cosine similarity and Siamese network NCAI TUKL Deep Learning Research Lab 01-Jun-2024 - 31-Aug-2024 1. Contributed to squash court ball tracking, addressing challenges such as occlusions and false positives through advanced augmentation and tracking techniques. 2. Implemented and evaluated image processing and tracking pipelines, orchestrating end-to- end evaluation scripts. 3. Developed a Pix2Pix GAN for generating building designs from sketches. 4. Improved image quality by

Machine Learning Engineer and final-year Software Engineering student at NUST, with strong industry and research experience in artificial intelligence, computer vision, and scalable machine learning systems. Experienced in building production-ready, low- latency ML pipelines and deploying end-to-end AI solutions in real-world environments. Technical Expertise: PyTorch, TensorFlow, Transformers, GANs, Computer Vision, NLP, Vector Databases MLOps & Cloud: AWS (Lambda, SQS, EC2, API Gateway, ALB, ASG), Docker, CI/CD, GPU acceleration (TensorRT) Systems & Performance: Multithreaded pipelines, queue-driven architectures, parallel CPU/GPU execution, inference optimization Applied Research: Autonomous AI agents, adaptive web interaction, reinforcement learning, temporal event detection Proven ability to bridge research and production, having reduced inference latency by ~40% in deployed systems and delivered scalable ML solutions across cloud-native architectures. Actively researching autonomous AI agents and adaptive web interaction, with a strong passion for applying cutting-edge AI research to solve complex, real-world problems with measurable impact.