Freelancers using PyTorch in Punjab

Freelancers using PyTorch in PunjabFreelancers using PyTorch in Punjab

Projects People

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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Cover image for Computer Vision - Detection and Segmentation with Yolo V9

Computer Vision - Detection and Segmentation with Yolo V9

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Cover image for Video Virtual Tryon

Video Virtual Tryon

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Cover image for LLM Agents Cybersecurity workflow

LLM Agents Cybersecurity workflow

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Rawalpindi, Pakistan

Microsoft certified data scientist

Microsoft certified data scientist

Cover image for T5 Finetuning for SQuAD Question Answering

T5 Finetuning for SQuAD Question Answering

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Cover image for CNN_alphabeta-pruning-chess-engine

CNN_alphabeta-pruning-chess-engine

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Cover image for IM07813/RealTime-Face-ID_opencv

IM07813/RealTime-Face-ID_opencv

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Cover image for MCTS-UCB Transformer Ensemble

MCTS-UCB Transformer Ensemble

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proMultan, Pakistan

Flutter Mobile Apps & AI/ML/RAG Engineer | Computer Vision

$5k+: Earned

4x: Hired

5.0: Rating

44: Followers

Flutter Mobile Apps & AI/ML/RAG Engineer | Computer Vision

Cover image for AI Gym Rep Counter: Automatic Workout Tracking System

AI Gym Rep Counter: Automatic Workout Tracking System

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Cover image for AI Sales Platform Development for XOutdoorLighting

AI Sales Platform Development for XOutdoorLighting

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Cover image for ProposalAI: Streamlining Proposal Writing with AI

ProposalAI: Streamlining Proposal Writing with AI

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Cover image for AI Customer Support Chatbot Development

AI Customer Support Chatbot Development

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Lahore, Pakistan

Top Rated Plus Freelancer & Top 1% Talent

$5k+: Earned

2x: Hired

5.0: Rating

16: Followers

Top Rated Plus Freelancer & Top 1% Talent

Cover image for AI Function Calling Agent

AI Function Calling Agent

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Cover image for Business Development AI Workflow

Business Development AI Workflow

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Cover image for ADHD-Friendly AI Automation Workflows

ADHD-Friendly AI Automation Workflows

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Cover image for Anchor Down - Transport & Logistics Platform

Anchor Down - Transport & Logistics Platform

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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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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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Lahore, Pakistan

Where Founder Vision Is Engineered into Agentic AI Products.

31: 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 After working with founders across

After working with founders across AI, SaaS, and automation projects, I realized one thing: Most great ideas don't fail because of the technology, they fail because turning them into a real product is incredibly complex. That's the problem I enjoy solving. From AI product strategy and system architecture to MVPs, agentic AI, workflow automation, and production-ready web and mobile applications, I help founders transform ambitious ideas into products that people actually use. I finally recorded a short introduction to share a little about who I am, what I build, and how I work with startups. If you're building an AI product, launching an MVP, or looking to automate your business with AI, I'd love to connect. Looking forward to collaborating with more founders and teams here on Contra. #AI #AgenticAI #Startup #SaaS #MVP #ProductDevelopment #Automation #WebDevelopment #MobileDevelopment #AIEngineering #Founders #Contra

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Cover image for I wish I had it

I wish I had it months ago. (Launching Proult - Desktop App) I spent 20 minutes looking for a Stripe credential. I checked my notes. My browser bookmarks. Old chats. Random text files. Not because I forgot it. Because I couldn't remember where I had saved it. That's when I realized the most dangerous phrase in a developer's workflow isn't: "I forgot." It's: "I've saved it somewhere." As freelancers, students, developers, and builders, we constantly juggle multiple projects simultaneously. And each project comes with its own ecosystem of information: • Client details • Credentials and passwords • API keys and secrets • Domains and hosting accounts • GitHub repositories • Deployment links • Meeting notes • Project requirements • Time logs and deadlines The problem isn't that we don't save this information. The problem is that we save it everywhere. -A Notepad file for credentials. -A spreadsheet for clients. -A project management tool for tasks. -Bookmarks for links. -Chat messages for "important" details. And before long, finding information takes more time than using it. After one too many "I know I saved this somewhere" moments, I decided to build something for myself. A single place where every project has its own secure workspace. Not just for storing passwords, but for managing everything related to that project: clients, credentials, API keys, notes, services, links, statuses, and time tracking. That's how "𝐏𝐫𝐨𝐮𝐥𝐭" started. So over the last few days, I've been building Proult, A local-first desktop application designed to keep everything related to a project in one place. -AES-encrypted credentials, API keys, and secrets -Project and client management -Built-in time tracking -Organization through project domains (Freelance, Personal, Organization, University) -Global search across projects, clients, credentials, and services -Full import/export support so your data always remains yours -Pinned projects, tags, notes, deployment links, and service management -Local-first architecture; no cloud dependency, everything stays under your control Still polishing it, but building it has already improved my own workflow significantly. Turns out, the best developer tools are often the ones built to solve your own frustrations first.

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Lahore, Pakistan

I build production-grade agentic AI systems, not demos.

I build production-grade agentic AI systems, not demos.

Cover image for A master's thesis titled "Global

A master's thesis titled "Global Optimization for PV-Integrated Smart Distribution Grids Using Data-Driven Neural Networks," supervised by Dr. Raheel Zafar and evaluated by Dr. Nauman Zafar Butt. The core idea is a teacher-student setup: an SOCP relaxation acts as a convex "teacher" that produces near-optimal solutions to a voltage-var optimization (VVO) problem, and a feedforward neural network (FCNN) is trained as a fast "student" surrogate that mimics the teacher at inference speed. A constraint-embedding trick using a tanh-bounded output layer cut infeasibility rates from 76.61% down to 0.13%. Validated on the IEEE 33-bus system using OpenDSS, with cyclical time encoding to capture daily/seasonal patterns in the grid data.

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Cover image for The before/after storefront structure captures

The before/after storefront structure captures the actual business model: a small business with no online presence gets a website built for free, and the ongoing relationship is the hosting revenue. That's why the arrow loops back to the laptop instead of just pointing left to right, it's meant to signal a continuing relationship rather than a one-off project delivery, which is the part of the business model that makes it sustainable.

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Cover image for This one is structured around

This one is structured around a single branching line rather than three separate scenes, since the three channels (speaks, reacts, tech) share one creator and one brand identity rather than being unrelated projects. The desk and ring-light setting grounds it in the actual production workflow (OBS, minimal editing), and each channel gets its own color and visual motif so the three feel distinct without breaking the sense that they're one connected thing.

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Cover image for This one is built around

This one is built around a command-center metaphor because that's functionally what an agentic triage system does, watches many streams, routes tickets, flags anomalies. The dashboard wall represents the fleet management platform itself (routes, trucks, live data). The small glowing "agent" orbs moving along connector lines are the LLM-driven agents doing the routing work in the background, and the one with a red pulse specifically calls out the fraud detection piece as a distinct, higher-stakes function within the same system.

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