AI Backend Engineer with 4 years of experience in backend development, DevOps, and applied machine learning. Skilled in building scalable cloud systems and LLM-powered workflows, with a focus on practical AI applications and automation.
Built PseudoCompiler (FastAPI, WebSockets, AWS) — scaled to 50k+ concurrent users with <50ms latency; cut infra spend ≈35%, allowing the company to serve more students on lower AWS bills.
Designed OptySleep engine (Redis caching, A/B tested) — boosted iOS app engagement by ~70%, helping the product team raise retention and improve subscription renewal rates.
Delivered IoT backends (Race Command on Django/Postgres/Redis) — processed live telemetry/signage with <100ms latency, improving race coordination.
Led AI/IoT backend workstreams for 3-engineer team (FastAPI, Docker, K8s); automated ops workflows (RAG + n8n), saving ~8 hrs/week and cutting MTTR by 30%.
Prototyped lightweight LLMs (quantized PyTorch/Ollama) for IoT devices reducing model size by ~50%, enabling secure on-device inference.
Architected hybrid systems (monolithic + microservices) on Azure with Kubernetes, scaling reliably to 100k-1M+ users.
Built ViralMe AI Video Editor (viralme.today), using Remotion, OpenCV, and FFMPEG reducing turnaround from hours to minutes.
Deployed self-hosted LLMs and Whisper3 via Ollama CLI, optimized GPU usage for cheaper inference.
Developed multi-agent video pipelines (YOLOv12, DeepSORT, SAM2 + RabbitMQ orchestration + LLM agents) to enable async detection, tracking and semantic summarization — achieved near-real-time throughput for production feeds (~20–25 FPS on target infra) and automated tagging.
Automated workflows and built connectors and background workers that cut manual reconciliation and data sync tasks by ~50%, improving reliability and reporting accuracy projects like Fortify ERP (fortify.biz).
Designed REST APIs & microservices (FastAPI/Django, Postgres, Redis, Firebase) and improved reliability & throughput.
Implemented CI/CD (GitHub Actions + Docker), shortened release cycles ~40%.
Migrated services to cloud infra; built dashboards improving issue detection by ~30%.
Built ML pipelines to support AI-powered breast cancer diagnosis, achieving 92% accuracy and reducing review time by 87%.
Worked in a lab incubation environment tied to FYP research in collaboration with Aga Khan University Hospital.
Developed tools for automated triage, sorting, and tagging of radiology images.
Prepared and analyzed large volumes of breath & cough data for early COVID-19 detection using signal processing.
Achieved accuracy of 89% in early-built prototype.
Developed a prototype of a COVID-19 detection solution using Flask & Ensembling techniques, under the supervision of Stanford University & DetectCovid.
Production-grade IoT backend system for real-time race event coordination. Processes live telemetry with <100ms latency, enabling race coordinators to manage concurrent events, control staging sequences, and broadcast safety flags to connected devices.
Built OptyAI—a custom recommendation engine from scratch that powers personalized sleep insights. Achieved ~70% boost in engagement, improved retention, and higher subscription renewals through Redis caching, A/B testing, and semantic similarity techniques.
Developed state-of-the-art inbound and outbound AI voice call agents for restaurants, realtors, pharma, and complaint dial systems. Integrated with custom CRM and software solutions, delivering natural conversational experiences powered by Retell AI.
Real-time video editor on the web using Remotion. Edit, preview, and render videos directly in the browser with AI-informed effects. Features include 2-minute transitions from long videos to multiple reels, motion graphics, background noise removal, and more.
Online platform for real-time AQA Pseudo to Python 3 translation with live execution and output visualization. Features complex lexical code translation with integrated AI chat for error detection and code assistance.
Built a Gemini-powered Legal AI Assistant with RAG (Retrieval-Augmented Generation) capabilities. Provides intelligent legal document analysis, question answering, and document retrieval using advanced language models and vector search.
Ali, U., Kandhro, I.A., Ahmed R.S., Khan, A.A. Shahbaz, M.H. Osama, M.
K. Mahboob, M. H. Shahbaz, F. Ali, and R. Qamar
VFAST trans. softw. eng., vol. 11, no. 2, pp. 249–255, Jun. 2023
M. H. Shahbaz, Zain-Ul-Abidin, K. Mahboob and F. Ali
2023 7th International Multi-Topic ICT Conference (IMTIC), Jamshoro, Pakistan, 2023, pp. 1-7
T. Mubeen, Zain-Ul-Abidin, M. H. Shahbaz, P. O. Roth and M. A. L. Nieto
2023 Global Conference on Wireless and Optical Technologies (GCWOT), Malaga, Spain, 2023, pp. 1-7
Shahbaz, M. H., Baig, R. W., Zain-ul-Abidin
ISBN: 9798390752586 – Amazon Kindle
M. H. Shahbaz, Z. Ul Abidin, U. Marfani, M. Abbasi, T. Mubeen
SSUET
Microsoft for Startups · Founders Hub (Transpify)
Awarded $25,000 in Azure Credits by Microsoft for Transpify startup under Founders Hub.
GDSC & Microsoft Learn Student Ambassadors
Mentored 1000+ students in AI/ML via workshops and community events at Google Developer Student Clubs and MLSA. – 2020-2022
Stay tuned
A reserved slot for upcoming awards, grants, and milestones.
