Freelance Backend Engineers in Lahore

Freelance Backend Engineers in LahoreFreelance Backend Engineers in Lahore

Projects People

proLahore, Pakistan

AI Automation Engineer | Full-Stack Apps & Integrations

66x: Hired

4.9: Rating

154: Followers

Expert

Expert

+2

AI Automation Engineer | Full-Stack Apps & Integrations

Cover image for Flyyt Time | Elevate your aviation career

Flyyt Time | Elevate your aviation career

5

103

Cover image for Gut Health SaaS Platform Development

Gut Health SaaS Platform Development

1

14

Cover image for Magnai | UK Public Affairs

Magnai | UK Public Affairs

6

87

Cover image for Humoni - secure housing in under 72 hours

Humoni - secure housing in under 72 hours

7

144

Backend Engineer(1)

proLahore, Pakistan

Full Stack Web & Mobile Dev | Next Js | AI Agent | React

2x: Hired

5.0: Rating

34: Followers

Full Stack Web & Mobile Dev | Next Js | AI Agent | React

Cover image for Northward Capital Investment Platform Development

Northward Capital Investment Platform Development

2

21

Cover image for Adelphi Stock Brokers: Secure Investment Platform

Adelphi Stock Brokers: Secure Investment Platform

1

14

Cover image for Chat Buddy – Real-Time Mobile Chat Application Development

Chat Buddy – Real-Time Mobile Chat Application Development

2

17

Cover image for Prodigi Studios

Prodigi Studios

1

13

Backend Engineer(2)

Abdul Razaq Ali

proLahore, Pakistan

Website & Game developer and designer.

$10k+: Earned

5x: Hired

5.0: Rating

10: Followers

Website & Game developer and designer.

Cover image for Lyrics Star is an interactive

Lyrics Star is an interactive mobile app I developed for both game and backend, designed to help users of all ages especially music lovers learn their favorite songs by tapping along to lyrics tiles while the music plays. Built to make music appreciation fun and accessible, it turns passive listening into an active, engaging literacy experience for kids and music enthusiasts alike. 🔹 Key Highlights: ✔️ Lyrics Tile Gameplay – Tap-to-interact mechanics synced with real-time song playback. ✔️ Vibrant, Intuitive UI – Designed to appeal to kids and music enthusiasts alike. ✔️ Full-Stack Development – End-to-end game and backend architecture built for smooth, seamless play. ✔️ Ad Mediation & Monetization – Integrated systems to sustainably monetize gameplay. ✔️ Cross-Platform Reach – Live on both Google Play and the Apple App Store. ✔️ Continuous Improvement – Ongoing feature development and bug fixes post-launch. 🌟 Impact: Since launch, Lyrics Star has achieved over 100,000 downloads across the Google Play Store and Apple App Store within just a few months. The strong user reception has reinforced a commitment to continuous refinement, with new features and improvements actively rolled out to enhance the experience. By blending music, gameplay, and interactivity, Lyrics Star stands out as a fun, effective tool for boosting music appreciation and literacy among young audiences.

0

266

Cover image for SPiR Health is a next-generation

SPiR Health is a next-generation HealthOS platform I developed across mobile and web to help users engineer flow state, extend peak hours, and prevent burnout. Built on the proprietary S.P.E.A.R. System (Smart Performance Enhancing Adaptive Routine), it transforms wearable and manually logged data into personalized daily routines for career, lifestyle, and health optimization. 🔹 Key Highlights: ✔️ Flow-State Mapping – Predict peak performance hours & sync with calendars. ✔️ Lifestyle Tracking – Sleep debt, fasting, hydration, and recovery cycles. ✔️ Guided Wellness – Breathwork, meditation, binaural beats, and habit cards. ✔️ Productivity Tools – Pomodoro timers, focus dashboards, and flow mode. ✔️ Gamified UX – Duolingo-inspired momentum system to drive engagement. ✔️ AI Agent – Integrated on the website to deliver insights and smart guidance. ✔️ Web Hub – Centralized onboarding, health insights, and personalized routines. 🌟 Impact: Backed by 74 months of metabolism research and NASA protocols, SPiR Health combines science, gamification, and AI into one platform. By reclaiming 15–25+ elite hours each week, it helps users prevent burnout, maximize performance, and adopt sustainable habits. The dual mobile + web ecosystem positions SPiR Health as a disruptive innovation in digital wellness and health-tech.

0

276

Cover image for Safrootics Game — Hybrid Pixel

Safrootics Game — Hybrid Pixel Art Adventure Web3/NFT Game • Cross-Platform • Top 10 Web3 Recognition A family-friendly, hybrid pixel art game designed for all ages, including children. This project successfully merges engaging Web2 gameplay with innovative Web3 elements to create an immersive, non-gambling experience available across Android, iOS, and PC. Game link: https://safrootics.com/

2

478

Cover image for F1 vr simulation

F1 vr simulation

0

2

Backend Engineer(3)

proLahore, Pakistan

Lead Full-Stack Engineer

$10k+: Earned

1x: Hired

5.0: Rating

17: Followers

Lead Full-Stack Engineer

Cover image for Rider Guider | Voice-Controlled Horse Riding Lessons App

Rider Guider | Voice-Controlled Horse Riding Lessons App

1

13

Cover image for SafeStart Medical - Patient Safety Records

SafeStart Medical - Patient Safety Records

0

19

Cover image for Athleads (Mobile App) - Revolutionizing Fitness Tracking

Athleads (Mobile App) - Revolutionizing Fitness Tracking

0

13

Cover image for MKV - Comprehensive Hospital Intranet System

MKV - Comprehensive Hospital Intranet System

0

17

Backend Engineer(5)

Lahore, Pakistan

3x Retool Certified · AI Dashboards & Automation

$1k+: Earned

1x: Hired

5.0: Rating

68: Followers

Agency Partner

3x Retool Certified · AI Dashboards & Automation

Cover image for I am eager to present

I am eager to present my project. I have created a Business Development & Brand Building Agent, which is a CRM designed to assist start-up businesses in their growth phase. Features include: 1. Managing the bidding process with proposal templates, FAQs, and custom proposals with an AI chatbot. 2. Convenient saving and easy copying of important links and demo videos 3. Lead Generation and follow-up management across different platforms and for varying employees. 4. Client work delivery management across different platforms. 5. AI-enhanced brand management with custom social media posts for various platforms. Try here (https://retoolproagency.retool.com/apps/1bcdb0f4-625e-11f0-b92d-ef3a85acc914/ActiveApps/Business%20Development%20Agent/sales_templates?_releaseVersion=latest).

2

558

Cover image for Admin Panel for ISP & User Management

Admin Panel for ISP & User Management

1

34

Cover image for Order Management Sytem

Order Management Sytem

1

25

Cover image for AI-Driven CRM for Sales Outreach

AI-Driven CRM for Sales Outreach

1

23

Backend Engineer(2)

Lahore, Pakistan

From Strategy To Digital Product

5.0: Rating

6: Followers

From Strategy To Digital Product

Cover image for ChatTeach | Student & Teacher Learning Marketplace Mobile App

ChatTeach | Student & Teacher Learning Marketplace Mobile App

1

2

Cover image for Alahdeen B2B Ecommerce Marketplace

Alahdeen B2B Ecommerce Marketplace

1

7

Cover image for Mohesr Degree Verification Platform

Mohesr Degree Verification Platform

1

12

Cover image for Salvus Care Services Website Design and Development

Salvus Care Services Website Design and Development

1

6

Backend Engineer(3)

Tajammal Hameed

proLahore, Pakistan

Senior Full Stack Engineer | Laravel & SaaS Applications

New to Contra

Senior Full Stack Engineer | Laravel & SaaS Applications

Cover image for TWT Neighborhood (Real Estate) Platform

TWT Neighborhood (Real Estate) Platform

0

1

Cover image for Bizroad Mobile (transportation & logistics) App

Bizroad Mobile (transportation & logistics) App

0

1

Cover image for Social Networking & Community Mobile App

Social Networking & Community Mobile App

0

1

Cover image for Cloud-Based School Management System

Cloud-Based School Management System

0

1

Backend Engineer(4)

Lahore, Pakistan

ML AI | Backend | Computer Vision | GenAI | LLM Agents

New to Contra

ML AI | Backend | Computer Vision | GenAI | LLM Agents

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

1

129

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

78

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

93

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

94

Backend Engineer(4)