Freelancers using Playwright in Lahore

Freelancers using Playwright in LahoreFreelancers using Playwright in Lahore

Projects People

Lahore, Pakistan

Manual & Automation QA Engineer | Web, API & End-to-End Test

New to Contra

Manual & Automation QA Engineer | Web, API & End-to-End Test

Cover image for SauceDemo E2E Automation – Playwright

SauceDemo E2E Automation – Playwright Python + PyTest | POM Framework | 6 Tests Automated Overview: Automated https://www.saucedemo.com using Playwright Python + PyTest + Page Object Model built from scratch. Test Coverage (6 Tests): TC01 Valid login with standard_user TC03 Locked out user error validation TC09 Add single item to cart (Backpack) TC11 Remove item from products page TC15 Checkout with missing first name validation TC17 Complete happy path checkout E2E Framework: POM with login_page, products_page, cart_page, checkout_page, conftest fixtures, pytest-html reports, video recording Execution: 6/6 Passed | GitHub: https://github.com/mohsin-ishfaq/saucedemo-playwright-automation Tech: Python, Playwright Sync API, PyTest, POM

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Cover image for StockTargetAdvisor – Investment Research &

StockTargetAdvisor – Investment Research & Stock Analytics Platform QA [Canada] | FinTech | NDA Protected Overview: Canadian FinTech platform delivering stock ratings, target-price predictions, market insights, ML-powered investment recommendations. Work I Did: Tested financial data accuracy: stock ratings (Buy/Hold/Sell), target prices, research reports, watchlists, recommendation engine Validated dashboard calculations: portfolio performance, gain/loss, dividend yield Verified ML-generated insights match UI presentation across 20+ user workflows Regression testing each sprint - 70+ test cases Defect tracking in Trello - 30+ bugs reported Production verification post-deployment Tools: Manual Testing, Functional/Regression, Trello Domain: FinTech/Investment Research

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Cover image for NexaCare – Global Healthcare Second-Opinion

NexaCare – Global Healthcare Second-Opinion Platform QA | Global | NDA Protected Overview: HIPAA-compliant platform enabling patients, doctors, hospitals worldwide to get second opinions from board-certified US specialists. 4 roles: Patient, Doctor, Hospital, Specialist Panel. My Role: Manual QA Engineer Work I Did: Tested complete workflows for 4 distinct roles with different permissions Patient flow: Registration, case submission with medical records upload (DICOM, PDF, images), payment, specialist selection, communication, report receiving Doctor/Hospital flow: Patient referral, case tracking, record viewing, second opinion request Specialist Panel: Case assignment, review, report generation, secure messaging Validated medical record management: Upload validation (file type, size), secure storage, viewer functionality, download with watermark Security & Compliance: RBAC testing - verified Patient cannot see other patients, Doctor A cannot see Doctor B's patients, Hospital admin only sees own hospital cases. Tested HIPAA requirements - PHI encryption, audit logs, session timeout Functional/Regression/Smoke each release cycle - 100+ test cases executed Created detailed bug reports in Jira & Trello - 40+ bugs including 2 critical HIPAA RBAC issues Production verification before releases Impact: Ensured zero HIPAA violations in production, stable releases for global users Tools: Manual Testing, Jira, Trello, BrowserStack, Chrome DevTools Domain: Healthcare Global | Compliance: HIPAA

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Cover image for ConsidraCare – Healthcare & Home

ConsidraCare – Healthcare & Home Care Management Platform QA [Canada] | Sep 2024 – May 2025 | NDA Protected - Approach Only Project Overview: Canadian home-care platform enabling agencies to manage caregivers, clients, scheduling, care plans, payroll, invoicing. 4 portals: Client, Caregiver, Admin, Operations. My Role: QA Engineer (Manual + Automation + Security) Work I Did: Independently tested all 4 portals covering complex workflows: caregiver onboarding with background checks, shift scheduling with conflict detection, care plan management (ADLs), payroll calculations with overtime rules, invoicing with insurance integration Designed and executed 150+ test cases: Functional, Regression, Smoke, Cross-portal, End-to-End Built automated regression suite from scratch using Selenium Python + PyTest - 80 test cases automated, reduced regression time from 2 days to 4 hours API Testing: Validated 30+ REST APIs using Postman - caregiver availability, shift assignment, payroll generation, client billing - checked status codes, response schema, error handling, auth Database Testing: SQL verification - validated payroll calculations match DB, schedule conflicts prevented at DB level, invoice totals, data integrity across portals Security Testing: Foundational web security using Kali Linux tools - XSS in care notes, SQL Injection in search, authentication bypass, brute-force on login, role escalation checks HIPAA-adjacent validation: Ensured caregiver can only see assigned clients, client PHI masked for unauthorized roles, audit logs for sensitive actions Bug Reporting: Logged 60+ bugs in Jira with steps, expected/actual, severity, video proof for critical. Worked with devs through fix verification Release verification: Production smoke testing before each release, preventing critical payroll bugs Impact: Reduced production bugs by 40%, regression time by 75% Tools: Selenium Python, PyTest, Postman, MySQL, Jira, Kali Linux (Burp Suite, OWASP ZAP), BrowserStack, GitHub Domain: Healthcare | Type: Web Application | NDA: No confidential screenshots or data shared - approach only

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Muhammad Ahmad Mansoor

Lahore, Pakistan

Engineering intelligent solutions with AI.

5.0: Rating

2: Followers

Engineering intelligent solutions with AI.

Cover image for I just completed a production-ready

I just completed a production-ready AI Lead Generation Agent designed to automate how businesses discover, verify, and export qualified leads across multiple data sources. This system replaces manual lead hunting with an intelligent, filter-driven pipeline that aggregates business data, verifies contact information, and delivers CRM-ready outputs in real time. 🔑 What this agent does: • Searches businesses by location, industry, and keywords • Aggregates data from Yellow Pages, Google Maps (API-ready), and extensible sources • Applies smart filters (company size, founding date, industry relevance) • Automatically verifies emails, phones, and websites • Deduplicates leads for clean datasets • Exports structured CSVs for sales & marketing teams • Supports real-time queries via a FastAPI backend • Schedules daily exports and weekly reports 🧠 Tech Stack Highlights: • Python + FastAPI (async, high-performance backend) • Selenium-based scraping with anti-bot handling • Modular lead source orchestration • Glassmorphism UI with real-time search • CSV-based persistence (lightweight & scalable) This project was built with real business use cases in mind — sales pipelines, outreach automation, and scalable lead discovery — not just experimentation. 🎥 Full walkthrough video: 👉 I’m actively building and sharing end-to-end AI systems focused on automation, data intelligence, and real-world impact. hashtag#AI (https://www.linkedin.com/search/results/all/?keywords=%23ai&origin=HASH_TAG_FROM_FEED) hashtag#ArtificialIntelligence (https://www.linkedin.com/search/results/all/?keywords=%23artificialintelligence&origin=HASH_TAG_FROM_FEED) hashtag#LeadGeneration (https://www.linkedin.com/search/results/all/?keywords=%23leadgeneration&origin=HASH_TAG_FROM_FEED) hashtag#Automation (https://www.linkedin.com/search/results/all/?keywords=%23automation&origin=HASH_TAG_FROM_FEED)hashtag#SoftwareEngineering (https://www.linkedin.com/search/results/all/?keywords=%23softwareengineering&origin=HASH_TAG_FROM_FEED) hashtag#FastAPI (https://www.linkedin.com/search/results/all/?keywords=%23fastapi&origin=HASH_TAG_FROM_FEED) hashtag#WebScraping (https://www.linkedin.com/search/results/all/?keywords=%23webscraping&origin=HASH_TAG_FROM_FEED)hashtag#SaaS (https://www.linkedin.com/search/results/all/?keywords=%23saas&origin=HASH_TAG_FROM_FEED) hashtag#Startup (https://www.linkedin.com/search/results/all/?keywords=%23startup&origin=HASH_TAG_FROM_FEED) hashtag#Entrepreneurship (https://www.linkedin.com/search/results/all/?keywords=%23entrepreneurship&origin=HASH_TAG_FROM_FEED)hashtag#TechProjects (https://www.linkedin.com/search/results/all/?keywords=%23techprojects&origin=HASH_TAG_FROM_FEED) hashtag#AIProjects (https://www.linkedin.com/search/results/all/?keywords=%23aiprojects&origin=HASH_TAG_FROM_FEED) hashtag#BuildInPublic (https://www.linkedin.com/search/results/all/?keywords=%23buildinpublic&origin=HASH_TAG_FROM_FEED)hashtag#BusinessGrowth (https://www.linkedin.com/search/results/all/?keywords=%23businessgrowth&origin=HASH_TAG_FROM_FEED) hashtag#SalesTech (https://www.linkedin.com/search/results/all/?keywords=%23salestech&origin=HASH_TAG_FROM_FEED) hashtag#B2B (https://www.linkedin.com/search/results/all/?keywords=%23b2b&origin=HASH_TAG_FROM_FEED)

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Cover image for 🚀 Built an AI Receptionist

🚀 Built an AI Receptionist for Healthcare — Here's What It Can Do Managing a healthcare front desk is challenging. Calls, appointment scheduling, patient questions, and administrative tasks often overwhelm staff, leading to long wait times and missed opportunities to deliver a great patient experience. To address this, I built an AI Receptionist MVP designed specifically for healthcare providers. It can answer calls, schedule appointments, manage inbound and outbound communication, and respond to patient inquiries—all while staying within appropriate clinical boundaries. Here's what it demonstrated during a live test with our demo clinic, Evergreen Community Health Center: 🧠 Intelligent Patient Conversations Rather than following a fixed script, the AI understands context and knows its limitations. ✅ Recognized that dermatology wasn't offered and suggested available services instead: General Practice Pediatrics Physical Therapy Dental Care ✅ Clearly avoided giving medical advice by responding: "As a receptionist, I'm not qualified to provide medical advice." ✅ Offered the appropriate next steps by either: Booking a GP appointment for an initial assessment, or Transferring the caller to clinical staff for medical questions. 📅 Smart Appointment Scheduling The AI schedules appointments based on both clinic policies and patient preferences. During the demo, it: Collected the patient's information. Suggested the next available appointment. Adjusted the booking when the patient requested a later time. Checked clinic operating hours automatically and successfully booked a 5:00 PM appointment before closing. 💳 Administrative Automation Beyond booking appointments, the AI also handled routine administrative tasks by: Explaining consultation fees. Answering policy-related questions. Confirming referral procedures. Collecting the patient's email for appointment confirmation. ⚡ Built for Scale Unlike a traditional reception desk, the AI can handle thousands of conversations simultaneously. That means: No busy signals No waiting queues No missed calls Better patient accessibility 24/7 ⚙️ Easily Customizable Every healthcare provider operates differently. The AI can be configured with: Clinic-specific services Staff availability Business hours Appointment rules FAQs Internal workflows making it adaptable to virtually any healthcare organization. This project demonstrates how conversational AI can streamline healthcare operations while allowing staff to spend more time focusing on patient care. I'm excited to continue expanding its capabilities with integrations such as EMR/EHR systems, multilingual support, voice biometrics, and intelligent call routing. 💬 If you're exploring AI solutions for healthcare, I'd love to connect and discuss how conversational AI can modernize patient engagement. #AI #ArtificialIntelligence #HealthcareAI #HealthTech #VoiceAI #AIReceptionist #Automation #ConversationalAI #PatientExperience #GenerativeAI #LLM #Innovation

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Cover image for Building the Future of Hiring:

Building the Future of Hiring: Real-Time AI Interviews I’m excited to share a demo of our AI-based recruiter automation tool, designed to streamline recruitment funnels with real-time video interviews, deep skill analytics, and cheating detection. In this video, I demonstrate the full candidate journey: 🔹 Backend Automation: The system extracts skills from my resume and matches them against the job description automatically. 🔹 Interactive Interview: I speak directly with "Higher Vision AI" about my experience in Computer Vision, GenAI, and Agentic AI across industries like security surveillance and e-commerce. 🔹 Technical Deep Dives: We discuss real-world challenges, such as implementing voice-to-voice interaction using LiveKit and how I adapted to new integration hurdles. 🔹 Instant Feedback: The session wraps up with a comprehensive dashboard displaying my interview score, resume score, summary, and skills audit. I also discuss how I stay ahead of trends using resources like daily.dev (http://daily.dev). 🎧 Note: Please excuse the slight echo on the agent's voice in this screen recording; it is a result of the recording setup, not the live system! #AgenticAI (https://www.linkedin.com/search/results/all/?keywords=%23agenticai&origin=HASH_TAG_FROM_FEED) #TalentAcquisition (https://www.linkedin.com/search/results/all/?keywords=%23talentacquisition&origin=HASH_TAG_FROM_FEED) #ComputerVision (https://www.linkedin.com/search/results/all/?keywords=%23computervision&origin=HASH_TAG_FROM_FEED) #PeopleAnalytics (https://www.linkedin.com/search/results/all/?keywords=%23peopleanalytics&origin=HASH_TAG_FROM_FEED) #VoiceAI (https://www.linkedin.com/search/results/all/?keywords=%23voiceai&origin=HASH_TAG_FROM_FEED) #FutureOfWork (https://www.linkedin.com/search/results/all/?keywords=%23futureofwork&origin=HASH_TAG_FROM_FEED) #SkillAssessment (https://www.linkedin.com/search/results/all/?keywords=%23skillassessment&origin=HASH_TAG_FROM_FEED) #InterviewIntelligence (https://www.linkedin.com/search/results/all/?keywords=%23interviewintelligence&origin=HASH_TAG_FROM_FEED) #LLM (https://www.linkedin.com/search/results/all/?keywords=%23llm&origin=HASH_TAG_FROM_FEED) #SecureHiring (https://www.linkedin.com/search/results/all/?keywords=%23securehiring&origin=HASH_TAG_FROM_FEED) #TechRecruitment (https://www.linkedin.com/search/results/all/?keywords=%23techrecruitment&origin=HASH_TAG_FROM_FEED)

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Cover image for I wanted to make an

I wanted to make an AI talk to me like a real human. Not a chatbot that waits for you to finish speaking. Not a voice note exchange. I wanted an AI that could listen while I spoke, interrupt naturally, and respond instantly — like a personal assistant or customer-care agent. Something that didn’t just reply, but could actually do things — send emails, check dashboards, automate tasks. So I started small. I used a basic text model with Kokui for speech-to-text. The first time it responded, it felt magical — until it started ignoring me mid-sentence, replying late, or not speaking at all. Latency was awful. It wasn’t a conversation; it was waiting for a robot to remember it existed. I upgraded to DeepGram, tuned the audio, and still, it felt disconnected. I wanted real-time. I wanted the AI to exist in time with me, not after me. So I went deep into research. GitHub, StackOverflow, documentation black holes, even different AI platforms — but nothing gave me the real-time link I needed. Then I found one line buried in a doc: “OpenAI uses LiveKit for real-time voice.” That changed everything. LiveKit works like Zoom — a room where participants join and talk. I built my agent to join a LiveKit room, then I joined as well. And for the first time, I wasn’t sending messages to a server — I was talking to an AI inside the same space. The first test stunned me. Latency dropped to about 50ms. The AI listened while I spoke, and responded instantly. For the first time, it felt alive. Then came a strange bug. The AI joined once, but couldn’t reconnect after leaving. I rewrote code, regenerated tokens, nothing worked — until I realized LiveKit doesn’t let you rejoin a room with the same token. I changed the room name and boom — it worked flawlessly. Now it runs inside LiveKit Sandbox, talking and listening in real-time. It can send emails, check dashboards, handle automation — all with almost zero delay. I started out trying to make a talking AI. What I built feels more like a digital employee — one that works, listens, and speaks in real time. And the best part? It doesn’t feel like the future anymore. It feels like now. #AI (https://www.linkedin.com/search/results/all/?keywords=%23ai&origin=HASH_TAG_FROM_FEED) #ArtificialIntelligence (https://www.linkedin.com/search/results/all/?keywords=%23artificialintelligence&origin=HASH_TAG_FROM_FEED) #LiveKit (https://www.linkedin.com/search/results/all/?keywords=%23livekit&origin=HASH_TAG_FROM_FEED) #VoiceAI (https://www.linkedin.com/search/results/all/?keywords=%23voiceai&origin=HASH_TAG_FROM_FEED) #AITools (https://www.linkedin.com/search/results/all/?keywords=%23aitools&origin=HASH_TAG_FROM_FEED) #MachineLearning (https://www.linkedin.com/search/results/all/?keywords=%23machinelearning&origin=HASH_TAG_FROM_FEED) #AIAssistant (https://www.linkedin.com/search/results/all/?keywords=%23aiassistant&origin=HASH_TAG_FROM_FEED) #AIInnovation (https://www.linkedin.com/search/results/all/?keywords=%23aiinnovation&origin=HASH_TAG_FROM_FEED) #TechDevelopment 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Lahore, Pakistan

Full Stack Developer · React, Next.js & AI · 4+ Years

Full Stack Developer · React, Next.js & AI · 4+ Years

Cover image for AuditWave: Unified Website Audit Tool Development

AuditWave: Unified Website Audit Tool Development

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Cover image for CertsLibrary Multi-tenant SaaS Platform

CertsLibrary Multi-tenant SaaS Platform

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Cover image for Bechly.pk — Classified Marketplace Platform (Web + React Native)

Bechly.pk — Classified Marketplace Platform (Web + React Native)

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Cover image for Sourcing Genie — React Native Mobile App

Sourcing Genie — React Native Mobile App

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Lahore, Pakistan

AI-Driven QA Engineer Who Ships Bug-Free Software, Faster

7: Followers

AI-Driven QA Engineer Who Ships Bug-Free Software, Faster

Cover image for Entity Onboarding Portal – Digital

Entity Onboarding Portal – Digital Agent Registration & Application Management The Entity Onboarding Portal is a secure self-service platform designed to streamline the onboarding process for title agents joining the Stewart Trusted Provider® network. Previously, onboarding information was collected manually by Area Sales Managers and entered into Microsoft Dynamics 365 CRM. The portal transformed this process by allowing agents to directly submit their information, upload required documents, and manage applications online, significantly reducing manual effort and improving data accuracy. The portal integrates with Microsoft Dynamics 365 CRM, automatically synchronizing submitted data into Entity and Individual Applications for review and approval. Area Sales Managers can review applications, verify documents, conduct background checks, and manage the onboarding lifecycle through CRM, creating a seamless end-to-end onboarding experience. Role & Impact – QA Engineer • Owned the end-to-end quality assurance efforts for the Entity Onboarding Portal. • By validating critical business processes and edge cases through structured testing in Azure DevOps, I helped achieve zero production defects at launch, ensuring a seamless onboarding experience for both agents and internal stakeholders. • Performed smoke, sanity, regression, and end-to-end testing across multiple releases, proactively identifying defects and maintaining a defect escape rate below 3% throughout the project lifecycle. Key Outcomes • Delivered the Entity Onboarding Portal with 0 production defects at go-live. • Increased test coverage through comprehensive test design and execution in Azure DevOps. • Maintained <3% defect escape rate, reducing release risk and improving platform reliability. • Validated seamless integration between the onboarding portal and Microsoft Dynamics 365 CRM, ensuring accurate and secure onboarding data processing.

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Cover image for Stewart Financial Services – Production Deployment Turnaround

Stewart Financial Services – Production Deployment Turnaround

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Cover image for SPECTR – Centralized Real Estate

SPECTR – Centralized Real Estate Data Intelligence Platform SPECTR is a centralized data repository platform developed for Stewart Title to unify and provide intelligent access to large-scale property and title data across the United States. It aggregates over 25 million Owner and Lender title policies along with comprehensive data on 140+ million parcels, covering nearly the entire U.S. population. The platform enables users to efficiently access, analyze, and retrieve critical real estate information including property characteristics, ownership details, addresses, and title policy records. By consolidating fragmented datasets into a single system, SPECTR empowers teams to perform faster research, gain deeper insights, and make more informed, data-driven decisions. Core Features: • Centralized repository for 25M+ title policies and 140M+ property records. • Property data lookup including ownership, address, and parcel details. • Physical characteristics and attributes of real estate assets. • Owner and lender title policy information access. • Unified search and data retrieval system. • High-scale data aggregation and management. • Support for real estate research and decision-making workflows. • Enhanced data visibility across U.S. property records. My Role: • Validated data integrity across 25M+ title policies and 140M+ property records using Snowflake, ensuring accuracy within a platform covering 99.9% of the U.S. population. • Performed API and database validation to verify accurate data synchronization and retrieval across large-scale property and title datasets. • Executed functional, regression, integration, and data validation testing for property search, ownership records, and title policy workflows. • Collaborated with Agile Scrum teams to identify and resolve defects early, improving release quality and platform reliability. • Delivered Sprint Review demonstrations to stakeholders, showcasing completed features and validating business requirements before release.

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Cover image for Bircle – B2B Marketplace &

Bircle – B2B Marketplace & Business Networking Mobile Application Bircle is a modern B2B marketplace and networking platform designed to help buyers and sellers discover, connect, and grow their business opportunities in one place. The application enables businesses to expand their professional network, engage with potential clients, and showcase products and services through an interactive social feed. Key features include buyer requirement postings, seller product/service listings, connection requests, real-time messaging, and a review system that promotes trust and transparency between businesses. The platform creates a dynamic environment where users can build meaningful business relationships, generate leads, and explore new opportunities tailored to their industry. Core Features: • Buyer requirement posting and discovery • Seller product/service promotion and listings • Business networking through connection requests • Real-time in-app messaging and communication • Ratings and reviews for sellers • Personalized business feed for engagement and visibility • User profile and network management My Role: Mobile Application Tester (QA Engineer) • Tested iOS and Android mobile applications across 20+ devices, ensuring 95% test coverage for critical business workflows. • Performed functional, UI/UX, localization, compatibility, and responsiveness testing. • Designed and executed comprehensive test scenarios in Zephyr, reducing bug leakage to below 3% across multiple releases. • Identified, documented, and tracked defects using Jira, contributing to a 98% on-time bug resolution rate. • Validated buyer-seller interactions, connection requests, posting workflows, messaging functionality, and review mechanisms. • Conducted regression testing before releases to ensure platform stability and seamless user experience. • Collaborated closely with developers and product stakeholders to resolve issues and improve product quality. Impact • Achieved 95% test coverage for critical features. • Maintained bug leakage below 3%. • Supported 98% on-time bug resolution. • Improved overall application stability and user experience across Android and iOS platforms.

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Tanveer Hussain

Lahore, Pakistan

AI Architect | n8n, Python, FastAPI, LLM, RAG, Voice AI

New to Contra

AI Architect | n8n, Python, FastAPI, LLM, RAG, Voice AI

Cover image for Multi-Portal Real Estate Automation (AutoListDR)

Multi-Portal Real Estate Automation (AutoListDR)

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Cover image for Multi-Tenant Voice AI System (GoHighLevel

Multi-Tenant Voice AI System (GoHighLevel + Retell AI)

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Cover image for Lead Scraping & Processing System

Lead Scraping & Processing System (BYO Lead Scraper)

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Cover image for Expert RAG Pipeline Development and Implementation Services

RAG Pipeline Implementation

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Lahore, Pakistan

QA Engineer | Test Automation, API, Web & Mobile Apps

QA Engineer | Test Automation, API, Web & Mobile Apps

Cover image for Automated Testing Suite | Selenium, Cypress, Playwright, API

Automated Testing Suite | Selenium, Cypress, Playwright, API

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Cover image for API Testing & Backend Validation | Postman, Rest Assured, SQL

API Testing & Backend Validation | Postman, Rest Assured, SQL

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Cover image for Manual QA Testing | Web, Mobile, Usability & Bug Reporting

Manual QA Testing | Web, Mobile, Usability & Bug Reporting

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