Description

Technical Project Lead | HoopadVision | Jan. 2024 – Apr. 2025

       Core competencies: AI/ML Deployment, Git, Docker, AI-Electronics Systems Integration, Technical Leadership, Technical Presentations, Client Communication, Problem-Solving, Innovation, Jira.

·  Led the development and successfully delivered computer vision and AI projects within the HBOX (the company’s product), including:

§ Smart Burglar Alarms: Improved system reliability and client satisfaction with a 70% reduction in false alarms by means of object detection. [Link]

§ Intelligent Parking: Accomplished License Plate Recognition (LPR) to work with mechanical gates to automate parking management for smooth entry and exit of vehicles. [Link]

§ Attendance and Security Systems: A contactless attendance system based on face recognition for better efficiency at the workplace and security, also adaptable for access control in warehouses and industrial sites. [Link]

§ Smart Fire Alarm System: A proactive fire and smoke detection service that enhanced the safety of industrial and residential areas by 80% due to early fire and smoke alerts. [Link]

§ AIOT (AI + IoT): A smart home control system using Persian Voice Commands and a Chatbot powered with NLP and LLMs to provide smooth interaction for getting connected and controlling home devices. [Demo Link]

·  PLC & Computer Vision Pipeline Integration: Designed and implemented a Python-based communication layer between Delta PLCs (Modbus & Ethernet protocols) and the computer-vision pipeline outputs; LPR results triggered automatic gate opening, while fire and burglar detections activated site alarms and an automated dialer/caller. [Link]

·  Led the end-to-end project lifecycle, from RFP response and proposal writing to technical leadership and final delivery, while managing schedules, cross-functional teams, risk mitigation, and client communication. Achieved first-attempt project approvals through efficient FAT/SAT processes, detailed documentation, and rapid productivity ramp-up, improving efficiency and customer satisfaction.

 

Computer Vision Engineer | HoopadVision | May 2023 – Jan. 2024    

Core competencies: Software Development, R&D, Performance Optimization, ML Pipeline Design, CCTV, Dataset Creation, Data Cleaning, Model Training, Model Optimization.

·  Improved Sales and Functionality in Surveillance Solutions: Enhanced the rate of product acceptance in the surveillance sector by 10%, identified key customer requirements, designed, and implemented all computer vision models and applications on Linux-Ubuntu to increase stability and security; used Docker containers for effective deployment and management:

§ Biometric Liveness Detection: Developed a custom machine learning model for detecting spoofing attempts (critical in CCTV security) with 99% accuracy, building end-to-end ML pipelines including image acquisition, data cleaning, labeling, training, testing, and deployment. [Link]

§ 2x Faster Face Detection Service: Managed to reduce face detection inference time by 50% by rewriting the code and integrating ONNX, CUDA, and TensorRT.

§ Face Super-Resolution: Deployed a state-of-the-art deep learning model to enhance low-resolution CCTV faces; this leads to better recognition in challenging lighting conditions.

§ Vehicle Color Recognition: Developed a deep learning model to robustly recognize the colors of vehicles captured from CCTV.

·  Evaluated CCTV and industrial camera/lens setups for License Plate Recognition (LPR) and Face Recognition systems and integrated industrial camera connectivity into company software, enhancing system flexibility and performance.