
2
1
Pakistan
1 year of experience
Software Engineer | SaaS Platforms | AI Automation & Agentic AI | Business Process Automation | Custom Web Applications Software Engineer with 6+ years of experience building SaaS platforms, business applications, automation solutions, and custom web applications. I design, develop, and maintain software solutions that help businesses improve operations, enhance user experiences, and support long-term growth. Throughout my career, I have developed and maintained a wide range of products, including SaaS platforms, AI-powered applications, e-commerce solutions, SEO technology products, and business systems. I have hands-on experience across the full software development lifecycle, including requirements analysis, system design, development, deployment, performance optimization, and ongoing maintenance. My technical background includes Laravel, MERN stack, API design and integration, CMS platforms, and modern web technologies. Beyond development, I have led projects, collaborated with cross-functional teams, mentored developers, and delivered software solutions that align with both business objectives and technical requirements. I value clear communication, practical problem-solving, clean architecture, and building reliable systems that create lasting value. My current focus is on SaaS platforms, AI automation, Agentic AI, business process automation, and custom web applications. I enjoy helping organizations streamline workflows, improve efficiency, and solve complex business challenges through scalable technology solutions. Core Areas of Expertise: • SaaS Platforms • AI Automation & Agentic AI • Business Process Automation • Custom Web Applications • API Integrations • Software Architecture & System Design • Performance Optimization • Technical Leadership & Team Collaboration I believe successful software is not just about technology. It is about understanding business needs, solving real problems, and delivering solutions that support sustainable growth.

RetainIQ is a churn-prevention copilot for online stores. It tells a retention manager which customers are about to stop buying, why, and what to do about each of them within a fixed budget. The problem. E-commerce stores lose revenue quietly: customers don't cancel, they just stop ordering. The warning signs, a late delivery, an angry support ticket, a two-star review in Spanish, are scattered across systems and languages, and nobody reads them all. Retention teams end up sending blanket discounts and wasting budget on customers who would have stayed anyway. What RetainIQ does. Reads every message on AMD. An LLM, DeepSeek-V4-Flash on AMD Token Factory, reads about 6,100 support tickets and reviews in English, Spanish and German, 50 per request and within the rate limit, extracting sentiment, complaint type and explicit threats to leave. 99% of signals come from the LLM. Predicts churn and explains it. A classical logistic-regression model combines order history with those signals. Text from AMD lifts ROC-AUC from 0.734 (orders only) to 0.808. Every score is explained per customer. Plans the campaign. An optimizer picks the best action per customer: a personal call, a discount or nothing. In the demo store it finds $27,218 at risk across 73 high-risk customers. A $500 budget reaches 57 customers and saves an expected $2,380 in revenue, a 99% ROI. Answers questions in plain English. A Copilot, running on AMD, calls these tools and shows a step-by-step trace. Its numbers always come from the model and optimizer, never from guesses. It suggests follow-ups and drafts win-back messages in each customer's language. Built for real teams: accounts with admin, analyst and demo roles saved conversations per-user daily AI limits that protect the AMD quota PostgreSQL guided tours with narration mobile layouts The same code runs on an AMD Instinct MI300X with ROCm and vLLM by changing one setting. Live demo: "Try the demo": https://retainiq-amd-churn-copilot.vercel.app
18 Oct 2026