Md. Abu Ammar · Backend & AI Systems Engineer
I build production systems at Masjid Solutions: payment infrastructure moving millions of dollars a year, AI-powered kiosk monitoring across 120+ devices, and .NET Aspire distributed platforms on Azure. MS CS researcher in quantum machine learning.My undergraduate thesis explored quantum machine learning — variational circuits and encoding methods on PennyLane simulators — and the research thread continues through my MS: Bangla POS tagging with knowledge distillation, multi-output CNNs, ensemble methods. I bring a production engineer's discipline to research code.Payment rails (Stripe, ACH, wallets) moving millions a year for 20,000+ users, Azure Vision AI watching 120+ kiosks, roughly 200 releases a year through CI/CD I architected — designed with DDD and vertical slices. The hero beside this text is a quantum classifier I wrote from scratch; drag the data points.
$MM+
annual payment volume supported
120+
kiosks monitored across 60+ orgs
20,000+
users served by payment systems
200+
production deployments per year
featured system
Cloud-native distributed app that watches a fleet of donation kiosks with Azure Vision AI — orchestrated by .NET Aspire, deployed by generated GitHub Actions.
background-job-system · 2026-07
Designed and built the background-job subsystem for the Partners.com.bd marketplace backend (.NET, ASP.NET Core, SQL Server) — a dedicated Hangfire worker with an atomic-enqueue outbox, a dead-letter queue, idempotent handlers, least-privilege SQL isolation, and OpenTelemetry-based observability. The single place all async work now lives.
[dotnet][hangfire][distributed-systems][reliability]
salesforce-sync-platform · 2025-03
Automated integration platform keeping payment systems and Salesforce CRM in sync — daily jobs, custom mappings, retries, and error recovery instead of manual entry.
[salesforce][integration][automation][dotnet]
ach-payment-integration · 2025-01
Secure ACH, Apple Pay, and Google Pay payment workflows for donation kiosks and recurring billing across Stripe and Authorize.Net.
[stripe][authorize-net][payments][dotnet]
ammar2022bangla · Directed research (CSE498), North South University · 2022
[nlp][bangla][bert]
Part-of-speech tagging for Bangla — a low-resource language whose main benchmark, Microsoft IL-POST, is severely class-imbalanced — using contextual embeddings from three Bangla BERT models. A decision tree proves less biased by the imbalance than a neural network, motivating an unusual distillation direction: treat the class counts in the tree's leaf nodes as a probability distribution and distill that "dark knowledge" from the tree into the neural student .
ammar2022quantum · B.Sc. thesis (CSE499), North South University · 2022
[quantum-ml][quanvolution][cvqnn]
A comprehensive review of the state of the art in quantum machine learning, paired with hands-on classification experiments: two first-generation hybrid quantum-classical models — a quanvolutional neural network on a gate-based simulator and a continuous-variable quantum neural network on a photonic simulator — trained on MNIST and compared head-to-head against classical baselines on accuracy and convergence.
I work where production engineering meets machine learning research — connecting business operations, software architecture, AI, payments, and cloud infrastructure into systems that run every day.
At Masjid Solutions I own systems across their full lifecycle: payment infrastructure (Stripe, ACH, Apple Pay, Google Pay) supporting millions of dollars in annual volume for 20,000+ users; KioskVisionAI, which watches 120+ donation kiosks across 60+ U.S. organizations with Azure Vision AI; and an automated Salesforce synchronization platform that eliminated manual CRM entry. I deploy constantly — roughly 200+ releases a year through CI/CD pipelines I architected — and design with DDD, vertical slice architecture, and clean architecture principles so the systems stay healthy long after they ship.
The research thread runs in parallel: my undergraduate thesis explored quantum machine learning — variational circuits, encoding methods, hybrid classical-quantum models on PennyLane simulators — and continues through my MS: Bangla POS tagging with knowledge distillation, multi-output CNNs, ensemble methods.
My mission: continuously improve systems, automate the repetitive, and innovate at scale.