Freelancers using PyTorch in PunjabFreelancers using PyTorch in Punjab
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Hammad Tahir's avatar
Hammad Tahir
Lahore, Pakistan
AI Developer & ML Engineer: Top-notch Expertise
AI Developer & ML Engineer: Top-notch Expertise
Cover image for Yolo v10 - Object Detection and tracking
Yolo v10 - Object Detection and tracking
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328
Cover image for Computer Vision - Detection and Segmentation with Yolo V9
Computer Vision - Detection and Segmentation with Yolo V9
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52
Cover image for Video Virtual Tryon
Video Virtual Tryon
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36
Cover image for LLM Agents Cybersecurity workflow
LLM Agents Cybersecurity workflow
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49
PyTorch(3)
Saud Saleem's avatar
Saud Saleem
proLahore, Pakistan
Top Rated Plus Freelancer & Top 1% Talent
$5k+
Earned
2x
Hired
5.0
Rating
18
Followers
Top Rated Plus Freelancer & Top 1% Talent
Cover image for AI Function Calling Agent
AI Function Calling Agent
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14
Cover image for Business Development AI Workflow
Business Development AI Workflow
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12
Cover image for ADHD-Friendly AI Automation Workflows
ADHD-Friendly AI Automation Workflows
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41
Cover image for Anchor Down - Transport & Logistics Platform
Anchor Down - Transport & Logistics Platform
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17
PyTorch(1)
Arslan Mehmood's avatar
Arslan Mehmood
Lahore, Pakistan
ML AI | Backend | Computer Vision | GenAI | LLM Agents
New to Contra
ML AI | Backend | Computer Vision | GenAI | LLM Agents
Cover image for AI Vision for Retail, Industrial
AI Vision for Retail, Industrial & Monitoring Workflows Overview I have built and deployed multiple real-world computer vision systems for industrial inspection, retail automation, and monitoring workflows. My responsibilities covered: 🔹 Dataset preparation and labeling 🔹 Object detection model training 🔹 Segmentation model training 🔹 YOLO-based detection and tracking 🔹 Image/video inference pipeline development 🔹 Model evaluation and threshold tuning 🔹 Production deployment support 🔹 Cloud server management and optimization 🔹 Building practical AI workflows for real-world operational environments Fish Quality Inspection System - lythium.cl (http://lythium.cl) I led the development of an advanced fish quality inspection solution for an industrial workflow. The system used image analysis to monitor fish quality and support automated fish sorting based on AI predictions. 🔹 Led the development of an advanced AI-powered fish quality inspection system for an industrial workflow. 🔹 Built an image analysis pipeline to monitor fish quality from production-line images. 🔹 Trained object detection models to identify fish and relevant visual quality indicators. 🔹 Trained segmentation models to support more detailed visual inspection of fish regions. 🔹 Designed the AI workflow to support automated fish sorting based on model predictions. 🔹 Worked on inspection logic that could classify or route fish based on quality-related outputs. 🔹 Designed the system for conveyor-belt usage, where images need to be processed consistently and reliably. 🔹 Focused on production issues such as image quality, camera consistency, lighting variation, and model reliability. 🔹 Helped convert visual inspection from a manual/rule-based workflow into an AI-supported inspection pipeline. 🔹 Built the system to reduce manual inspection effort and improve production workflow efficiency. Shelfr.ai (http://Shelfr.ai) - Retail Automation Platform I developed AI image solutions for retail automation and execution. The system handled large-scale product detection across 10,575+ SKUs, price tag detection, shelf and display type detection, and gap detection for empty shelf spaces. 🔹 Developed large-scale AI image solutions for retail automation and execution. 🔹 Worked on product detection across 10,575+ SKUs, where each SKU represented a unique product. 🔹 Built object detection workflows to identify products from retail shelf images. 🔹 Developed price tag detection to locate and extract price label areas from store images. 🔹 Worked on shelf and display type detection to understand the retail environment layout. 🔹 Built gap detection logic to identify empty shelf spaces and out-of-stock areas. 🔹 Supported computer vision workflows for retail compliance, shelf monitoring, and store execution. 🔹 Worked with high-volume image data and production-level inference requirements. 🔹 Managed high-load production servers on Google Cloud Platform. 🔹 Implemented load balancing and autoscaling to improve system stability under production traffic. 🔹 Focused on scalable AI infrastructure capable of handling real-world retail image workloads. 🔹 Helped create AI systems for inventory visibility, shelf condition monitoring, and retail execution analytics. lake-shield.com (http://lake-shield.com) - USA LAKES - Boat Detection & Inspection System 🔹 Worked on a YOLO-based boat detection, tracking, and monitoring system. 🔹 Labeled datasets for boat detection and inspection model training. 🔹 Prepared image/video data for object detection training workflows. 🔹 Trained YOLO object detection models to detect boats in monitoring footage. 🔹 Built a detection pipeline capable of identifying boats from visual data. 🔹 Worked on boat tracking logic to monitor boat movement across frames. 🔹 Supported inspection and monitoring workflows using computer vision predictions. 🔹 Developed an end-to-end pipeline from labeled data to trained model and inference output. 🔹 Focused on practical model performance in outdoor environments where lighting, distance, angle, and background can vary. 🔹 Helped build a monitoring system that could support automated detection and review instead of fully manual observation. My Responsibilities Across These Projects 🔹 Led AI/computer vision system development 🔹 Designed labeling and dataset preparation workflows 🔹 Trained YOLO/object detection models 🔹 Trained segmentation models where needed 🔹 Built image and video inference pipelines 🔹 Evaluated models using practical production metrics 🔹 Improved model performance through dataset cleanup, retraining, and threshold tuning 🔹 Integrated AI models into backend or operational workflows 🔹 Supported production deployment and infrastructure optimization 🔹 Worked with real-world constraints such as lighting, camera angle, image quality, latency, and false detection rates Technologies Used 🔹 Python 🔹 YOLO / YOLOv8 🔹 Object Detection 🔹 Image Segmentation 🔹 OpenCV 🔹 PyTorch 🔹 FastAPI 🔹 Google Cloud Platform 🔹 Linux Servers 🔹 Load Balancing 🔹 Autoscaling 🔹 Custom Data Labeling Workflows 🔹 Model Training 🔹 Model Evaluation 🔹 Inference Pipeline Development 🔹 Production AI Deployment
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Cover image for LakeShield - AI-Powered Video Monitoring
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: 🔹 Designing the end-to-end AI and video-processing architecture 🔹 Building YOLO-based boat and vehicle detection pipelines 🔹 Developing object tracking and movement-analysis workflows 🔹 Implementing OCR for extracting boat registration information 🔹 Creating scalable pipelines for processing thousands of surveillance videos 🔹 Developing FastAPI backend services and automated data workflows 🔹 Building a Next.js analytics dashboard integrated with Supabase 🔹 Deploying and operating the AI pipeline on cloud GPU infrastructure 🔹 Optimizing model accuracy, inference speed, infrastructure costs, and reliability 🔹 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
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Cover image for Shelfr - AI-Powered Retail Shelf
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: 🔹 Designing the complete computer vision and backend architecture 🔹 Building product detection, shelf analysis, OCR, and image-processing pipelines 🔹 Developing APIs and scalable data-processing workflows 🔹 Deploying and managing production systems on GCP cloud servers 🔹 Optimizing model accuracy, processing speed, and infrastructure performance 🔹 Managing production monitoring, reliability, troubleshooting, and ongoing improvements 🔹 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
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Cover image for ⚖️ Built a French Legal
⚖️ 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: 🔹 Custom legal document ingestion and chunking 🔹 Metadata-based vector search 🔹 Hybrid retrieval and reranking 🔹 Agentic RAG workflows using LangGraph 🔹 Source-grounded answers with legal references 🔹 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
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PyTorch(1)
Usman Haider's avatar
Usman Haider
Lahore, Pakistan
AI/ML & Data Solutions Engineer
New to Contra
AI/ML & Data Solutions Engineer
Cover image for Fine-tuned OpenAI Whisper model on
Fine-tuned OpenAI Whisper model on domain-specific medical audio data to improve transcription accuracy for clinical and healthcare use cases. The project involved preprocessing medical speech datasets, handling noise and terminology challenges, and optimizing the model for improved recognition of medical vocabulary, accents, and context-heavy conversations. Delivered a robust speech-to-text system capable of producing highly accurate, structured transcriptions suitable for documentation, reporting, and downstream healthcare applications.
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Cover image for Retail Knowledge Graph In this project,
Retail Knowledge Graph In this project, we built a semantic knowledge graph tailored to the retail industry. The pipeline involved developing AI agents to transform heterogeneous data into standardized formats. Ontologies were created to represent domain knowledge accurately. Using Gemini models and LangChain, user queries were converted into Cypher queries to retrieve insights from a Neo4j database. We utilized an MCP server for orchestration and LangSmith for secure login and audit trails. This system enhances complex data exploration for non-technical users.
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Cover image for Student Medical Chatbot Built a chatbot
Student Medical Chatbot Built a chatbot to assist MBBS students in navigating medical literature. Leveraged Llama Index and fine-tuned language models to ensure accuracy. Embeddings were stored in OpenSearch, hosted on AWS. The Django backend included secure authentication and session management for a robust user experience.
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Cover image for Prompt Engineering Mini-Academy is a
Prompt Engineering Mini-Academy is a digital learning product built using Kajabi. It helps users learn how to write better AI prompts and use AI tools for daily tasks such as writing, research, summarization, and productivity. The problem it solves is that many people use AI tools without a proper structure, which leads to weak or generic results. This product gives users a clear learning path, practical prompt templates, and workflow examples to improve the quality of their AI outputs. I used Kajabi to create the landing page, email capture form, downloadable prompt resource, product offer, checkout page, and course structure. A sample video is attached to demonstrate the product flow and user experience.
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PyTorch(1)
Ayesha Javed's avatar
Ayesha Javed
Lahore, Pakistan
Where Founder Vision Is Engineered into Agentic AI Products.
47
Followers
Where Founder Vision Is Engineered into Agentic AI Products.
Cover image for Out of Harm's Way – AI-Powered Code Security MLOps System
Out of Harm's Way – AI-Powered Code Security MLOps System
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Cover image for Taskail – AI-Powered Voice-to-Task Desktop App
Taskail – AI-Powered Voice-to-Task Desktop App
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Cover image for Personalized Tutoring in AI, Automation, and Digital Workflows
I have been promoted to "Super Tutor" on Preply.😍 -5 Star Rating✨ -70+ Lessons Completed -30+ Students taught Click 👇 to: Get 30% off your trial lesson with me. (https://preply.com/en/?pref=MzEzODYxMTI=&id=1790865324.975789&ep=w1) Syllabus included: - AI - Automations - Agentic AI - GitHub/Linkedin Optimization - AI Filmmaking - AI Workflows - Business Workflows - AI Consulting Let me guide you from where you are, assess your level, design your personalized syllabus, and start learning!
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Cover image for Ghibli-Style Milestone Animation The Context/Problem: I need...
Ghibli-Style Milestone Animation The Context/Problem: I needed a unique visual asset to celebrate reaching 15,000 followers on LinkedIn and showcase my creative AI capabilities. The Solution/Execution: I built a custom Google AI workflow to generate a 10-second, four-scene animated short film styled entirely in a Studio Ghibli aesthetic. The Value Delivered: The video merged technical prompting with storytelling, generating much impressions within hours and proving AI's power for scroll-stopping brand assets.
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160
PyTorch(2)
Arham Malik's avatar
Arham Malik
Rawalpindi, Pakistan
Backend & AI engineer who ships systems fast and scale.
New to Contra
Backend & AI engineer who ships systems fast and scale.
Cover image for Yeah, this one is a
Yeah, this one is a bit out there, but it was super fun. I built an AI that actually understands memes. It looks at the image and the text together using PyTorch, TensorFlow, and GPT 4. I used early and late fusion techniques to combine vision and language, plus Tesseract OCR to grab text from the image itself. The model hits over 95% accuracy on meme classification. It’s not your typical corporate project, but it shows how multimodal AI can understand humor, culture, and context. Definitely one of my favorite experiments. For more details, please visit: https://arham-nexus.vercel.app/work/memechecker
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Cover image for This is a classic enterprise
This is a classic enterprise academic portal, but built solid. It uses  Web Forms, ASP.NET, C#, and SQL Server. The main challenge was role based access control because you have admins, faculty, TAs, and lab demonstrators all needing different permissions. I designed a three tier architecture with stored procedures and optimistic concurrency control so data doesn’t get messed up when people edit at the same time. Over 200 users per semester use it for task assignments and progress tracking. It’s not flashy, but it works perfectly. For more details, please visit: https://arham-nexus.vercel.app/work/talabportal
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Cover image for Skill Swap is a peer
Skill Swap is a peer to peer learning platform where you can be both a student and a teacher. I built the backend with Java Spring Boot, real time chat with WebSockets, and video calls with WebRTC. The matching algorithm finds people based on skills, ratings, and availability. You can switch roles anytime. It also has a trust based review system so fake reviews don’t ruin it. This was an MVP, but it proved that real time learning communities can work really well. For more details, please visit: https://arham-nexus.vercel.app/work/skillswap
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Cover image for This is a native Android
This is a native Android app I built with Kotlin and Firebase. It connects tourists with verified local guides in real time. You open the app, see nearby guides, book a tour, and then you can track each other’s location live using Google Maps. I also added offline support because travel doesn’t always have perfect internet. Over 100 active users ended up using it,during the MVP. The best part? Battery usage was optimized so your phone doesn’t die halfway through the day. Really proud of how smooth it turned out. For more details, please visit: https://arham-nexus.vercel.app/work/raaheraast
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PyTorch(1)