Chief Technology Officer
Arif is the CTO of Vector Labs, bringing deep experience in machine learning, deep learning, computer vision, OCR, document intelligence, object detection, RAG systems, and production AI deployment. His work has supported enterprise clients across telecom, banking, fintech, retail visibility, healthcare recommendation, security, and anti-counterfeit systems.
Technical Leadership
Arif leads the technical architecture direction for AI-heavy systems at Vector Labs. His experience spans model development, OCR pipelines, computer-vision systems, backend deployment, Dockerized services, cloud infrastructure, and applied AI systems that solve measurable business problems.
Enterprise AI Experience
Built document verification systems using EAST text detection for layout analysis and HTR for handwriting recognition.
Impact: Helped reduce customer onboarding costs for telecom clients by 92.5%, showing how AI can directly lower operational cost at enterprise scale.
Developed AI-based parsers to extract structured data from central bank reports for financial institutions.
Impact: Reduced financial statement processing time from 3 business days to around 30 minutes.
Developed object detection systems to identify and count festoons, posters, and stickers in retail shops.
Impact: Helped optimize promotional material distribution and contributed to 98% visibility in major POSM categories across Bangladesh.
Designed and implemented a data warehouse for large-scale sensor data.
Impact: Enabled structured downstream analytics and reporting from high-volume time-series data.
Developed a steganography-based system to embed secret codes within General Health Warning images on cigarette packets.
Impact: Supported automated detection of authentic versus illicit products and helped combat market counterfeiting.
Developed an AI-powered chatbot that recommends doctors and clinics based on user location and health concerns.
Impact: Simplified the search process by quickly recommending nearby healthcare providers.
Developed a real-time object and activity detection system for driver safety use cases.
Impact: Explored how computer vision can identify dangerous objects and violent activity to improve safety response workflows.
Research Background
Created a dataset of 13,720 manually annotated disaster images and developed a Three-Layer Attention Model achieving over 95% accuracy on unseen test data.
Worked on disaster type and severity classification using deep learning, VGG-16, Inception-V3, ResNet, and attention visualization.
Investigated deep-learning methods for disaster assessment using real-time social media images.
Technical Stack
Vector Labs brings together research, AI architecture, and software delivery to build systems that make business operations clearer, faster, and more intelligent.