Arslan Mehmood
ML AI | Backend | Computer Vision | GenAI | LLM Agents
New to Contra
Arslan is ready for their next project!
LakeShield - AI-Powered Video Monitoring and Vessel Intelligence Platform
I led the development of LakeShield as the Senior AI/ML Engineer and Lead Developer, taking the platform from initial research and experimentation to a scalable production system.
My responsibilities included:
š¹ Designing the end-to-end AI and video-processing architecture
š¹ Building YOLO-based boat and vehicle detection pipelines
š¹ Developing object tracking and movement-analysis workflows
š¹ Implementing OCR for extracting boat registration information
š¹ Creating scalable pipelines for processing thousands of surveillance videos
š¹ Developing FastAPI backend services and automated data workflows
š¹ Building a Next.js analytics dashboard integrated with Supabase
š¹ Deploying and operating the AI pipeline on cloud GPU infrastructure
š¹ Optimizing model accuracy, inference speed, infrastructure costs, and reliability
š¹ Managing production monitoring, troubleshooting, maintenance, and continuous improvements
The platform transforms raw surveillance footage into structured operational insights, enabling automated vessel monitoring, vehicle activity analysis, registration extraction, and reporting.
This project involved complete technical ownership across Computer Vision, AI/ML, backend development, cloud infrastructure, data engineering, MLOps, and production operations.
#ComputerVision #VideoAnalytics #ArtificialIntelligence #ObjectDetection #OCR #MLOps #FastAPI #NextJS #Supabase #CloudEngineering
2
80
Shelfr - AI-Powered Retail Shelf Intelligence Platform
I led the development of Shelfr as the Senior Computer Vision Engineer and Lead Developer, taking the platform from the initial idea and system architecture through development, deployment, and production operations.
My work included:
š¹ Designing the complete computer vision and backend architecture
š¹ Building product detection, shelf analysis, OCR, and image-processing pipelines
š¹ Developing APIs and scalable data-processing workflows
š¹ Deploying and managing production systems on GCP cloud servers
š¹ Optimizing model accuracy, processing speed, and infrastructure performance
š¹ Managing production monitoring, reliability, troubleshooting, and ongoing improvements
š¹ Leading technical decisions across AI, backend, cloud infrastructure, and DevOps
The platform converts real-world retail shelf images into structured product and shelf-level insights, helping automate retail auditing, product visibility analysis, and inventory workflows.
#ComputerVision #RetailAI #LeadDeveloper #AIEngineering #GCP #MLOps #Python #CloudEngineering
2
62
āļø Built a French Legal AI Assistant powered by advanced RAG and LLM technology.
The system enables users to ask complex legal questions and receive accurate, context-aware answers grounded in French legal documents.
Key features include:
š¹ Custom legal document ingestion and chunking
š¹ Metadata-based vector search
š¹ Hybrid retrieval and reranking
š¹ Agentic RAG workflows using LangGraph
š¹ Source-grounded answers with legal references
š¹ Private deployment on an Azure VM using locally hosted LLMs
The main focus was improving retrieval accuracy, reducing hallucinations, and making large collections of legal documents easier to search and understand.
#LegalAI #RAG #LLM #ArtificialIntelligence #LangGraph #Azure #GenerativeAI #MachineLearning
1
62
AI-Powered PDF Data Extraction
My role: AI Data Processing and Extracton Engineer
Organizations often struggle to extract structured and useful information from large volumes of unstructured PDF documents.
I developed a flexible AI-powered data extraction solution that allows users to define the specific entities and fields they want to retrieve. The system processes different PDF formats, identifies relevant information, and converts it into structured, usable data.
The solution reduces manual document processing, improves retrieval accuracy, and can be adapted to different document types and business requirements.
A working demo link is attached.
0
61