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Md. Abu Ammar · Backend & AI Systems Engineer

Backend & AI systems engineer. I ship payment rails, distributed platforms — and train quantum circuits.

I build and run Partners.com.bd — a live marketplace and business-social platform across Bangladesh and the UK: its .NET API, Flutter apps, and legacy MVC→API migration. Before that, at Masjid Solutions, payment infrastructure moving millions of dollars a year and AI-powered kiosk monitoring across 120+ devices 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.I own the .NET API, Flutter apps, and MVC→API migration behind Partners.com.bd — a live marketplace across Bangladesh and the UK. Before that at Masjid Solutions: payment rails (Stripe, ACH, wallets) moving millions a year for 20,000+ users, Azure Vision AI across 120+ kiosks, roughly 200 releases a year through CI/CD I architected — DDD and vertical slices throughout. The hero beside this text is a quantum classifier I wrote from scratch; drag the data points.

▶ or watch an AI interview this site

$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

KioskVisionAI

Cloud-native distributed app that watches a fleet of donation kiosks with Azure Vision AI — orchestrated by .NET Aspire, deployed by generated GitHub Actions.

read the case study →

120+ kiosks60+ orgs, USBlob StorageQueuesVision AIanomaly detectionnotify adminshijack alertsauto-recoverreboot · heal
Fig. 1 — 120+ kiosks · Azure Vision AI · automated recovery

Selected work

all work →

background-job-system · 2026-07

Background Job System — Dedicated Worker, Outbox, Dead-Letter Queue

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]

auth-architecture · 2026-07

Authentication Architecture — One Core, Two Edges, Every Session Revocable

Designed the authentication architecture for the Partners.com.bd marketplace (ASP.NET Core Identity, MediatR, SQL Server) around three principles: one core decides *who you are*, each client edge gets the session artifact safest for its medium (HttpOnly cookie for the server-rendered web, bearer JWT + rotating refresh for native mobile), and every session — web or mobile — must be revocable and enumerable server-side. Architected across two accepted ADRs after a four-stream R&D review: one decision core, two transport edges, revocable sessions on both, and a JWKS issuer boundary — with the single-core convergence of the user logins and a full Critical/High hardening set under 832 tests.

[dotnet][security][identity][oauth]

payments-platform · 2026-06

Payments Platform — One Generic Ledger, Self-Healing Settlement

Designed and built the payments platform for the Partners.com.bd marketplace backend (.NET, ASP.NET Core, SQL Server) — one generic `dbo.Payment` money ledger that serves every paid feature, a vendor-agnostic `IPaymentGateway` abstraction with a country-aware resolver, a single self-healing settlement coordinator, and a Hangfire reconciliation backstop. It replaces a legacy wide table (`dbo.OnlinePayment`) that stored money as text and trusted the client's callback.

[dotnet][payments][distributed-systems][reliability]

ammar2022bangla · Directed research (CSE498), North South University · 2022

Bangla POS Tagging Using Supervised Learning and Knowledge Distillation

[nlp][bangla][bert]

abstract ▸

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 .

[read distilled][pdf][case study]

ammar2022quantum · B.Sc. thesis (CSE499), North South University · 2022

Machine Learning In The Realm Of Quantum: The State-Of-The-Art, Challenges, Future Vision and Applications Of It

[quantum-ml][quanvolution][cvqnn]

abstract ▸

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.

[read distilled][pdf][case study]

About

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.

Today I lead Web & App Development at Partners Online (Bangladesh & UK), the company behind Partners.com.bd. I design and run the platform end to end — a .NET API, the Flutter apps, and a legacy MVC→API migration — deploying constantly and designing with DDD, vertical slice, and clean architecture principles so the systems stay healthy long after they ship.

Before that, at Masjid Solutions, I owned 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 — roughly 200+ releases a year through CI/CD pipelines I architected.

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.

more about me →