M Huzaifa Siddiq

📧 huzaifa62012@gmail.com

🌐 linkedin.com/in/muhammad-huzaifa-2a3821249

🏠 Karachi, Pakistan

I am a Software Engineer who builds whole products: the mobile app, the backend and dashboards behind it, the AI features inside it, and the infrastructure that ships it. I work across React Native and Expo, TypeScript and Next.js, Python (FastAPI, Django) and NestJS, with PostgreSQL and Redis underneath and AWS and Azure deployments behind CI/CD.

🚀 2026: five apps built, four live in the stores. At The Monk Co. I built HeadQuarters CRM as sole engineer (app, website and backend; live on the App Store), built the Wash42 apps, admin panel and Progressive Web App and most of its backend (live on the App Store and Google Play), and built core features of Dugan Psychiatry (live on Google Play). At Rehani Soko I built Hani, an AI property concierge, app and backend (live on the App Store). Three more store submissions are in review, and ProperTkt, a freelance product, is in development. Details and store links are on the Projects page.

A good part of my work is AI. Hani searches listings across portals, negotiates rent with agents over WhatsApp inside the seeker's budget, and searches by photo. Claude reads agents' replies, and every answer is validated before Hani acts on it. I also ran Rehani's LLM layer: requests route across Anthropic and OpenAI model tiers with per-request cost tracking, assistants keep long-term user memory and handle voice through Whisper and TTS, and AI-generated tour videos pass an automated quality gate before they publish.

Two things I am glad about outside the day job. 🏆 3rd Prize in the AI Track ($15,000) at the Solana Breakout Hackathon, from 8,300+ entries, for Agent Arc, a non-custodial AI trading terminal on Solana where I led the frontend. And two co-authored papers, one on 6D pose estimation for robotic grasping (arXiv:2411.13205) and one on real-time product segmentation on low-cost edge devices (DOI:10.13140/RG.2.2.34475.96800).

Technically, I specialize in:

Mobile: React Native, Expo (EAS Build, Submit, Update), Expo Router, offline sync, push notifications, WebRTC, App Store & Google Play releases
Frontend: React, Next.js, Vue.js, Nuxt, TypeScript, Vite, Tailwind CSS, Redux Toolkit, TanStack Query, PWAs
Backend: FastAPI, Django, NestJS, Node.js, Celery, REST/OpenAPI, Socket.IO
Data: PostgreSQL, SQL Server, MongoDB, Redis
AI/LLM: Anthropic & OpenAI APIs, multi-model routing, vision, structured output, RAG, LangChain, LangGraph, embeddings, LLM evals, Whisper
Cloud & DevOps: AWS (CDK, EC2, S3, CloudFront, SES), Azure, Docker, GitHub Actions, GitLab CI, Vercel, Sentry
Testing: pytest, Jest, Playwright, Maestro, coverage gates in CI

What interests me most is the part that starts after the prototype works: getting a product through review, into users' hands, and reliable, measurable and cheap enough to keep running. Open to Software Engineer, Full-Stack, Mobile and AI Engineer roles, and to contract work.

Latest Blog Posts

Jul 10, 2025	Agent Arc Takes 3rd Prize in the AI Track at Solana Breakout Hackathon!
Jun 30, 2025	How I Automated Data Collection Across Global Real Estate Markets
Jun 29, 2025	Teaching Robots to Grasp: Our Journey Into 6D Pose Estimation and Warehouse Automation

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Selected Publications

An Integrated Approach to Robotic Object Grasping and Manipulation

Aug 2023

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In response to the growing challenges of manual labor and efficiency in warehouse operations, Amazon has embarked on a significant transformation by incorporating robotics to assist with various tasks. While a substantial number of robots have been successfully deployed for tasks such as item transportation within warehouses, the complex process of object picking from shelves remains a significant challenge. This project addresses the issue by developing an innovative robotic system capable of autonomously fulfilling a simulated order by efficiently selecting specific items from shelves. A distinguishing feature of the proposed robotic system is its capacity to navigate the challenge of uncertain object positions within each bin of the shelf. The system is engineered to autonomously adapt its approach, employing strategies that enable it to efficiently locate and retrieve the desired items, even in the absence of pre-established knowledge about their placements. Crucially, the robotic system operates with a paramount emphasis on autonomy. The intricate interplay of algorithms, control mechanisms, and sensor fusion empowers the robot to execute the entire object picking task without human intervention. This unfaltering commitment to autonomy is a pivotal step towards revolutionizing warehouse operations, potentially paving the way for unprecedented levels of efficiency and productivity. This project serves as a testament to the intersection of robotics, computer vision, and artificial intelligence in tackling a complex challenge within the realm of modern logistics. The envisioned robotic system represents a significant advancement in autonomous object-picking technology, holding the promise of transforming conventional warehousing practices. As the fusion of cutting-edge technology and logistical innovation unfolds, the outcomes of this endeavor have the potential to redefine the future of warehouse operations and automation within the industry.
Smart Shelf Advertising using Real-Time Product Segmentation and Interaction on Low-Cost Edge Devices

Owais Ahmed, Uzair Abid, Hamza Ali Khan , and 2 more authors

Jul 2025

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Engaged in exciting research projects with upcoming publications in the pipeline.