CTO & AI Systems Architect
Qalab
Hassnain
Backend · Deep Learning · LLMs · Computer Vision · Data Science · IoT · Cloud
Qalab
Hassnain
CTO & AI Systems Architect
Education
- MS Computer Science
- Centre for Advanced Studies in Engineering (CASE)
- B.E. Computer Engineering
- National University of Sciences and Technology (NUST)
Who I Am
CTO at Quickgen Technologies, with 4+ years building production AI systems. I focus on deep learning, LLM pipelines, and computer vision. Additionally, I bring expertise in real-time data, MLOps, and cloud deployment on AWS and Azure. As a result, I've shipped 13+ products from idea to launch — leading teams across healthcare, hospitality, fintech, and consumer tech. My technical foundations are backed by 16 certifications in machine learning, data science, and NLP.
Certifications
- Neural Networks and Deep Learning — Coursera
- AI for Medical Diagnosis — Coursera
- Applied Data Science with Python Specialization — Coursera
- Introduction to Data Science in Python — Coursera
- Applied Machine Learning in Python — Coursera
- Tools for Data Science — Coursera
- Applied Text Mining in Python — Coursera
- Open Source Tools for Data Science — Coursera
- Applied Social Network Analysis in Python — Coursera
- Data Science Orientation — Coursera
- Applied Plotting, Charting & Data Representation in Python — Coursera
- Deep Learning with Python — Udemy
- Python 3.6 Complete Course — Udemy
- Mastering Interview Skills — Udemy
- Programming in C# — Udemy
- Microsoft Office Specialist Word 2013 — Microsoft
AI, ML & Deep Learning
20 tools- Deep Learning
- Neural Networks
- TensorFlow
- Keras
- YOLOv8
- OpenCV
- LLMs
- Whisper
- Deepgram
- Gemini API
- GPT-4
- RAG
- NLP
- Text Mining
- LSTM
- scikit-learn
- ElevenLabs
- Sentence Transformers
- Replicate API
- Prompt Engineering
Data Science & Analytics
11 tools- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Jupyter
- Data Visualisation
- scikit-learn
- ChromaDB
- Vector DBs
- Model Fine-tuning
Backend & APIs
12 tools- FastAPI
- Flask
- Django
- .NET Core
- C#
- Node.js
- WebSockets
- REST APIs
- Microservices
- gRPC
- MQTT
- UDP
Cloud & Infrastructure
9 tools- AWS
- GCP
- Azure
- Vercel
- Docker
- Kubernetes
- CI/CD
- Redis
- Nginx
Databases & BaaS
7 tools- PostgreSQL
- SQL Server
- MongoDB
- Firebase
- Supabase
- ChromaDB
- Vector DBs
Observability & DevTools
7 tools- Grafana
- Sentry
- Glitchtip
- Prometheus
- Docker Compose
- GitHub Actions
- Postman
Frontend & Mobile
6 tools- React
- Next.js
- Flutter
- React Native
- Tailwind
- TypeScript
IoT & Hardware
7 tools- BLE 5.0
- ESP32
- MQTT
- Edge AI
- PCM Audio
- FFmpeg
- 200Hz+ Streaming
Production AI Insights
How to Deploy a Computer Vision Model to Production
Most CV tutorials end at model training. This guide covers every layer I put in place before any vision model goes live …
Building Real-Time IoT Systems with BLE and WebSockets: Lessons from 200Hz+ Sensor Streaming
The hardest part of building wearable tech isn't the AI. It's the 200 milliseconds between the sensor and the screen. Fo…
LLM-Powered Real-Time Audio Pipelines: How We Built AI Transcription at Scale
Most developers think the hard part of voice AI is the speech-to-text model. It isn't. The hard part is everything aroun…
Chief Technology Officer
- Architected a real-time audio communication system replacing walkie-talkies in hotels, cutting staff response time by ~45%.
- Built PCM audio ingestion pipeline with Whisper & DeepGram achieving 94%+ transcription accuracy and ~88% intent precision via Gemini LLM.
IoT & Full Stack Developer
- Deployed complete backend & web-app for a two-sided physiotherapy platform, reducing clinician onboarding time by ~60%.
- Integrated ML-based kinematics pipeline — gait analysis and kinetics models achieving 92%+ movement classification accuracy from IoT wearable sensors.
Chief Technology Officer
- Leading technical strategy and end-to-end delivery across AI, IoT, and SaaS products in healthcare, hospitality, fintech, and consumer tech.
- Shipped CCTV anomaly detection system (YOLOv8) with 91% accuracy across 8+ simultaneous camera feeds, reducing false alerts by 35%.
Freelance (Upwork & Direct)
AI Engineer & Backend Developer
- Delivered 15+ AI and backend solutions for international clients — computer vision (OCR, pose estimation, object detection), NLP automation pipelines, and full-stack web apps.
CareCloud
Information Technology Intern
- Built health services REST APIs in .NET Core C# for a live healthcare production system.
PTCL
Software Engineer Intern
- Developed an Employee Record Search desktop application for HR using Python and deep learning.
Writing
Latest
Posts
Most CV tutorials end at model training. This guide covers every layer I put in place before any vision model goes live — API design, containerisation, versioning, monitoring, and cost optimisation.
The hardest part of building wearable tech isn't the AI. It's the 200 milliseconds between the sensor and the screen. Four years of lessons from production IoT systems — BLE reconnection, protocol selection, edge preprocessing, and monitoring.
Most developers think the hard part of voice AI is the speech-to-text model. It isn't. The hard part is everything around it — the audio ingestion pipeline, the LLM classification layer, the WebSocket architecture, and the operational infrastructure that keeps it all running under production load.
Every item on this checklist exists because I once shipped without it. Seven layers — crash reporting, analytics, UX feedback, bug tracking, infrastructure monitoring, device fingerprinting, and CDN — that I now run before any AI system goes live.
Production RAG fails in specific ways the tutorials skip. I built PaperIntel — a research intelligence system with citation-level accuracy — using hybrid retrieval, cross-encoder reranking, and systematic evaluation. This is what the full architecture actually looks like.
Your production AI model is probably 4x bigger than it needs to be. I reduced inference time from 340ms to 91ms and cut monthly cloud costs by 60% using INT8 quantization — without changing a single model layer. Here's the full pipeline.
Most microservices migrations are driven by architectural fashion rather than specific engineering pain. Ours was driven by a measurable scaling problem. This is the story of migrating a live platform without downtime, what broke in ways we didn't anticipate, and what 3x throughput actually looks like.
YOLOv8 benchmarks are well documented. What's not documented is what happens when you process 8 simultaneous CCTV feeds in real time, apply zone-based business rules, and deliver WebSocket alerts under 200ms while keeping false positives low enough that security staff actually trust the system.