Amirhossein Shabaninejad (Amir Nejad)

I am a backend engineer by training and experience, with a strong focus on building maintainable and scalable systems. My core stack revolves around C#, .NET, and ASP.NET Core, where I rely on clean architecture, domain-driven design (DDD), and CQRS to manage complexity in long-lived systems. I have worked extensively with relational databases such as PostgreSQL and SQL Server in production environments.

Most of my recent work has been in the financial domain, particularly in payment systems where reliability, consistency, and fault tolerance are critical. I have been involved in designing APIs, integrating with external providers, and evolving systems that operate under real-world constraints such as high load, partial failures, and strict availability requirements.

Alongside my industry work, I am pursuing a Master’s degree in Computer Science with a focus on Artificial Intelligence. My research is centered on MLOps and real-time computer vision, specifically exploring how to design systems that can adapt their behavior based on input conditions and system constraints.

Over time, my interests have shifted toward problems that require both solid engineering and analytical thinking—especially where system-level decisions directly impact the behavior of machine learning models. I aim to continue working in this intersection, whether in advanced engineering roles or research-oriented environments.

Building intelligent systems in production is not just a modeling problem—it is a systems problem. Latency, resource limits, failure scenarios, and data quality all influence how models behave in practice. Ignoring these aspects often leads to solutions that work in isolation but fail under real-world conditions.

In my work, I try to make these trade-offs explicit. Instead of relying on a single performance metric, I focus on understanding when a simpler approach is sufficient and when a more complex solution is justified. This applies both to backend systems and to machine learning pipelines.

I am particularly interested in designs where decisions are transparent and measurable. Whether it is routing between services or selecting between models, I prefer approaches that can be reasoned about, evaluated, and improved over time rather than treated as black boxes.