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Schedule Interview NowMy name is Aqeel A. and I have over 2 years of experience in the tech industry. I specialize in the following technologies: Computer Vision, Python, Deep Learning, Image Segmentation, Object Detection & Tracking, etc.. I hold a degree in Bachelor of Engineering (BEng), Master of Engineering (MEng). Some of the notable projects I’ve worked on include: Stable Diffusion-Based Video Stylization (VidToMe), Text to Video/Image Generation Mobile App, AI-Powered Interview Assessment, Text to Video/Image Generation Web App, Transcription and Speaker Diarization with Whisper & PyAnnote, etc.. I am based in Haripur, Pakistan. I've successfully completed 10 projects while developing at Softaims.
I am a business-driven professional; my technical decisions are consistently guided by the principle of maximizing business value and achieving measurable ROI for the client. I view technical expertise as a tool for creating competitive advantages and solving commercial problems, not just as a technical exercise.
I actively participate in defining key performance indicators (KPIs) and ensuring that the features I build directly contribute to improving those metrics. My commitment to Softaims is to deliver solutions that are not only technically excellent but also strategically impactful.
I maintain a strong focus on the end-goal: delivering a product that solves a genuine market need. I am committed to a development cycle that is fast, focused, and aligned with the ultimate success of the client's business.
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Techlogix
Deployed VidToMe (CVPR 2024) — a Stable Diffusion-based video style transfer model — on RunPod’s serverless GPU platform. Refactored the original code, built a custom Docker container, and exposed an API for zero-shot video editing using text prompts. Enables real-time video stylization with minimal infrastructure setup.
1. Developed a mobile application for text-to-image and text-to-video generation 2. Built the backend using FastAPI and deployed on AWS 3.Configured CI/CD pipeline for streamlined deployment 4. Deployed generative models on serverless GPU infrastructure 5. Finetuned LoRA models to support various artistic styles 6. Designed and implemented a database for user credentials, subscription plans, and credit tracking
Developed an MVP for an AI-powered interview assessment system 1. Built the backend using Flask and deployed on GCP Cloud Run 2. Configured CI/CD pipeline for automated deployment 3. Developed computer vision models for gaze tracking, head pose estimation, and posture recognition 3. Implemented algorithms for text-to-speech, speech-to-text, audio quality analysis, pitch, and loudness detection 4. Used OpenAI and Retrieval-Augmented Generation (RAG) to generate topic-relevant questions and score candidate responses
Developed a web application for text-to-image and text-to-video generation 1. Developed backend using Flask and deployed on GCP Cloud Run 2. Configured CI/CD pipeline for automated deployment 3. Deployed generation models on Runpod serverless infrastructure 4. Finetuned LoRA models for various visual styles 5. Implemented database for user credentials, payment plans, and credits 6. Integrated backend APIs with frontend
Developed a serverless pipeline for generating accurate transcriptions with word-level timestamps and speaker diarization using OpenAI’s Whisper model and PyAnnote-audio. Integrated both models into a unified workflow and deployed on RunPod’s serverless GPU infrastructure. The system identifies individual speakers and aligns spoken words with precise timing, making it ideal for podcasts, interviews, meetings, and media analytics applications.
Bachelor of Engineering (BEng) in
2015-01-01-2019-01-01
Master of Engineering (MEng) in AI and Autonomous Systems
2021-01-01-2023-01-01