Haris Zahid

Washmen needed to scale its operations efficiently while maintaining service quality, managing complex logistics, and ensuring smooth payment and customer support workflows across a growing user base.

I redesigned the platform using a microservices-based architecture, enabling independent service scaling and significantly improving system reliability. As part of this, I built dedicated services for billing, orders, catalog, and promotions—each modular and independently deployable, making the system easier to maintain and scale over time. I also integrated key 3rd-party platforms to streamline payments, delivery operations, and customer communication, creating a more efficient and responsive ecosystem.

To improve back-end performance, I introduced asynchronous processing patterns that allowed the system to handle high-traffic workloads without compromising reliability. I integrated 3rd-party APIs across payments, logistics, and customer support, optimizing how these services communicated with the core platform. Throughout the process, I focused on clean, modular service design, ensuring each component could be developed, deployed, and scaled independently, reducing friction for both the engineering team and end users.

Blood donation organizations struggled with fragmented systems for donor management, donation tracking, and engagement, leading to inefficient operations and low donor retention.

To address the issue, I developed a multi-tenant ERP platform that enables blood centers to efficiently manage donor data, analyze trends, and improve communication, ultimately creating a more streamlined and rewarding donation experience.

The platform was built on a multi-tenant architecture. At its core, it featured centralized donor management and donation tracking, giving teams a unified view of donor activity and campaign performance. Real-time analytics provided insights into donation trends and engagement patterns, supporting more informed decision-making. To improve retention, I built a donor engagement and reward system. The solution was also designed to be integration-ready.

I built responsive and scalable dashboard interfaces using React and developed reusable UI components for complex ERP workflows. I integrated front-end with back-end APIs to ensure seamless data flow, collaborating with back-end team to optimize system performance. I also improved UX for data-heavy dashboards and user interactions
impact.

Businesses and vendors needed a scalable platform to manage products, inventory, and sales while delivering a smooth and reliable shopping experience to users across a large catalog.

I built and enhanced a multi-vendor eCommerce platform that enables vendors to onboard, manage inventory, and sell products while providing customers with a fast, intuitive, and seamless shopping experience.

The platform supported multi-vendor product and inventory management within a single, unified system. Advanced filtering and real-time API-driven data rendering kept the experience fast and accurate, while an optimized checkout flow and scalable architecture ensured reliable performance under high traffic.

I built and optimized React-based UI components and integrated REST APIs with a Python back end to enable dynamic, real-time data rendering. I refined the product discovery and navigation experience, making targeted UI/UX enhancements and creating a smoother, more intuitive journey from browsing to checkout.

The result was a faster, smoother user experience with stronger product discoverability and customer engagement. The front-end architecture became more scalable and maintainable, improving usability for both vendors and customers alike.

Nejo AI is an AI SaaS platform that converts long-form documents into podcast-style audio using OCR, LLM-based summarization, voice generation, and asynchronous cloud processing. Users can upload documents, generate structured summaries, and receive high-quality audio that can be listened to, downloaded, or shared.

I helped design and implement a scalable back-end architecture using Django, Node.js, PostgreSQL, AWS SQS/SNS/S3, FFmpeg, and Selenium. I worked on queue-based processing, AI workflow integration, media handling, and fault-tolerant delivery to ensure the platform could reliably process document workloads without blocking the user experience.

The result was a production-ready AI automation platform that reduced content consumption time, improved document accessibility, and delivered a reliable audio generation workflow at scale.

PodSkim is an AI-powered SaaS platform that makes long-form podcast content searchable, skimmable, and easier to consume. The platform uses LLMs, semantic search, vector embeddings, and NLP pipelines to help users quickly discover summaries, key insights, and relevant moments from podcast episodes.

I worked on React and TypeScript interfaces, AI-driven API integrations, semantic search workflows, and scalable serverless back-end functions. I also contributed to NLP-based summarization, content retrieval flows, and analytics/event tracking to improve user engagement and product insights.

The result was a scalable AI media platform that reduced content discovery time, improved podcast engagement, and gave users a faster way to extract value from long-form audio content.