Freelancers using PyTorch in LahoreFreelancers using PyTorch in Lahore
ProjectsPeople
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
0
321
Cover image for Computer Vision - Detection and Segmentation with Yolo V9
Computer Vision - Detection and Segmentation with Yolo V9
0
51
Cover image for Video Virtual Tryon
Video Virtual Tryon
0
27
Cover image for LLM Agents Cybersecurity workflow
LLM Agents Cybersecurity workflow
0
44
PyTorch(3)
Saud Saleem's avatar
Saud Saleem
proLahore, 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
3
12
Cover image for Business Development AI Workflow
Business Development AI Workflow
0
2
Cover image for ADHD-Friendly AI Automation Workflows
ADHD-Friendly AI Automation Workflows
3
36
Cover image for Anchor Down - Transport & Logistics Platform
Anchor Down - Transport & Logistics Platform
2
13
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.
0
85
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.
2
2
82
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.
0
53
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.
1
65
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
1
47
Cover image for AI-Powered PDF Data Extraction My role:
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
27
Cover image for AI Agents & RAG Chatbots
AI Agents & RAG Chatbots with Persistent Memory I design and build intelligent AI agents and chatbots that maintain conversation context, retrieve reliable information, and interact with external tools and APIs. Core Capabilities 🔹 Persistent conversation and long-term memory 🔹 RAG-powered answers with reduced hallucinations 🔹 Tool calling, APIs, web search, and file retrieval 🔹 Multi-agent and multi-step workflows 🔹 Integration with OpenAI, Claude, Gemini, and open-source LLMs 🔹 Vector databases including pgvector, Pinecone, Weaviate, FAISS, and ChromaDB Technologies LangGraph, LangChain, Agno, PydanticAI, Haystack, FastAPI, OpenAI, Claude, Gemini, Hugging Face, PostgreSQL, pgvector, Pinecone, and Weaviate
0
25
Cover image for French Legal AI Assistant &
French Legal AI Assistant & Agentic RAG System Overview I designed, built, and deployed a specialized Legal AI Assistant for French lawyers using agentic RAG, legal data pipelines, vector search, reranking, open-source LLMs, and citation-grounded answer generation. The system allowed lawyers to ask legal questions and receive answers grounded in French law articles, legal references, and relevant judicial cases. Problem / Challenge Legal data is very different from normal document data. A generic RAG pipeline using fixed-size chunks often breaks legal meaning, misses important context, or retrieves incomplete references. The main challenges were: 🔹 Legal documents had different structures and lengths 🔹 Articles and laws could not be randomly split into fixed-size chunks 🔹 Each answer needed traceable legal references 🔹 Retrieval had to understand legal scope, not just semantic similarity 🔹 The system needed to reduce hallucinations for legal users 🔹 Deployment had to respect privacy and regulatory requirements My Expertise I worked as the Lead AI Engineer / Agentic RAG Developer responsible for the complete system design and implementation. My responsibilities included: 🔹 Legal data pipeline architecture 🔹 Document parsing and preprocessing 🔹 Custom legal chunking strategy 🔹 Vector database design 🔹 Agentic RAG workflow development 🔹 Retrieval optimization and reranking 🔹 Open-source LLM deployment 🔹 Backend API development with FastAPI 🔹 Secure Azure cloud deployment 🔹 Multi-tenant system support French Legal Data Engineering Pipeline I built an automated ETL pipeline to process thousands of French legal documents, articles, and judicial cases. The pipeline handled: 🔹 Raw legal document ingestion 🔹 Text cleaning and normalization 🔹 Legal article extraction 🔹 Section-aware document structuring 🔹 Custom chunk generation 🔹 Metadata extraction for article number, article title, section, source, and reference 🔹 Embedding generation 🔹 Vector database ingestion 🔹 Repeatable updates for future legal data expansion The chunking strategy was designed so legal articles were not cut in the middle or separated from their meaning. Agentic RAG Workflow Instead of using a simple one-step vector search, I built a LangGraph-based agentic RAG workflow. The workflow included: 🔹 User query understanding 🔹 Legal intent detection 🔹 Legal domain and scope identification 🔹 Generation of 2–5 targeted legal search queries 🔹 Retrieval of relevant chunks for each query 🔹 Deduplication of repeated results 🔹 Reranking of retrieved legal evidence 🔹 Source-grounded answer generation This improved tested retrieval accuracy from around 50% to 95%+. Retrieval, Citations & Case Law The retrieval system was designed to make answers transparent and verifiable. I implemented: 🔹 Vector search for semantic legal retrieval 🔹 Reranking to improve relevance 🔹 Metadata-based source traceability 🔹 Citation-backed answer generation 🔹 Article-level legal references 🔹 Typesense-based retrieval for French judicial cases 🔹 Supporting case law returned with legal answers This allowed lawyers to verify the exact legal source behind each generated response. Open-Source LLM & Cloud Deployment I evaluated and deployed open-source LLM infrastructure for private legal AI usage. The deployment included: 🔹 Qwen2.5:14B for French legal reasoning 🔹 Ollama and vLLM for model serving 🔹 Embedding and reranker models on a private Azure GPU VM 🔹 NVIDIA T4 16GB GPU optimization 🔹 Python/FastAPI backend APIs 🔹 Secure Azure deployment in the France region 🔹 Multi-tenant isolated access 🔹 GitHub CI/CD and Linux server management The system was designed for privacy, reliability, and regulatory compliance. Technologies Used 🔹 Python 🔹 FastAPI 🔹 LangChain 🔹 LangGraph 🔹 LangSmith 🔹 Ollama 🔹 vLLM 🔹 Qwen2.5:14B 🔹 ChromaDB 🔹 Typesense 🔹 Vector Databases 🔹 Reranking Models 🔹 Embedding Models 🔹 Azure Cloud 🔹 Linux 🔹 GitHub CI/CD Impact 🔹 Built a production-ready legal AI assistant for lawyers 🔹 Improved retrieval accuracy from ~50% to 95%+ in tested scenarios 🔹 Reduced hallucinations through citation-grounded generation 🔹 Enabled lawyers to verify answers using article and case references 🔹 Created a scalable legal data pipeline for thousands of documents 🔹 Deployed private open-source LLM infrastructure for legal compliance 🔹 Delivered a strong foundation for future legal AI workflows
0
54
PyTorch(1)
Talha Ubaid's avatar
Talha Ubaid
Lahore, Pakistan
AI Engineer and Machine Learning Expert
New to Contra
AI Engineer and Machine Learning Expert
Cover image for Urdu News AI Summarization &
Urdu News AI Summarization & Search System Designed and developed an end-to-end AI platform for processing Urdu news from television broadcasts and digital media. The system automatically transcribes speech, extracts text from news tickers, generates concise summaries, and enables intelligent semantic search across large collections of Urdu news content.
0
0
Cover image for The system continuously monitors examination
The system continuously monitors examination halls through live camera feeds, analyzes student behavior, and flags suspicious activities such as mobile phone usage, abnormal head movements, gaze deviation, multiple faces, and unauthorized interactions.
0
7
Cover image for AI Surveillance & Activity Recognition
AI Surveillance & Activity Recognition System Built a real-time AI-powered surveillance platform for intelligent monitoring, object detection, and security analytics. The system leverages deep learning and computer vision to detect people, vehicles, unattended objects, and suspicious activities across multiple camera streams. Key Features Real-time person, vehicle, and object detection Automatic Number Plate Recognition (ANPR) Facial recognition and identity verification Multi-object tracking using ByteTrack Unattended baggage and boundary breach detection Crowd monitoring and suspicious activity recognition Real-time alerts and monitoring dashboard Optimized for high-performance GPU deployment
0
3
Cover image for AI Chatbot with RAG &
AI Chatbot with RAG & Multi-Agent System
0
56
PyTorch(3)
Ayesha Javed's avatar
Ayesha Javed
Lahore, Pakistan
Where Founder Vision Is Engineered into Agentic AI Products.
New to Contra
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
1
1
Cover image for Taskail – AI-Powered Voice-to-Task Desktop App
Taskail – AI-Powered Voice-to-Task Desktop App
1
1
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.
1
47
Cover image for Cute Girly Aesthetic Laptop Wallpaper Design
Cute Girly Aesthetic Laptop Wallpaper Design
1
0
PyTorch(2)
Irtaza Ahmed Khan's avatar
Irtaza Ahmed Khan
Lahore, Pakistan
Machine Learning Engineer
Machine Learning Engineer
Cover image for Wasail: Demand Forecasting System
Wasail: Demand Forecasting System
0
6
Cover image for Time Series Forecasting
Time Series Forecasting
0
5
Cover image for Natural Language Processing
Natural Language Processing
0
3
Placeholder project card media
View more →
PyTorch(2)