I'm Abdalrahman Bashir, a systems-focused software engineer. I graduated with a Bachelor of Science in Computer Science from the University of Missouri-Kansas City. I have a deep passion for backend development, distributed systems, and infrastructure-level problem solving. I specialize in building production-ready microservices, scalable APIs, and real-time data processing pipelines using modern technologies within the .NET, C++, and Python ecosystems.
My work involves a broad spectrum of areas, particularly focusing on the development of high-throughput gRPC-based MapReduce systems using C# and .NET 9, and deploying full-stack applications with React, TypeScript, and OpenAI's large language models. As a Research Assistant at UMKC, I contributed to network simulations and system performance research. It involved combining PyTorch, C++, and Linux-native tools to assess mobility and communication strategies in real-time environments.
I prioritize clean architecture, performance optimization, and a developer-first approach to observability. My development workflow is built on a solid foundation of software engineering principles, including Domain-Driven Design, Test-Driven Development, and CI/CD automation using Azure DevOps and GitHub Actions. I am also proficient in SQL and NoSQL data models, cloud-native deployments on Azure, and container orchestration with Docker.
I approach each project with an engineer's discipline and a builder's mindset. I am particularly attracted to challenges involving distributed coordination, efficient resource utilization, and systems-level thinking.
Developed end-to-end, scalable RESTful APIs using C#, .NET Core, Entity Framework Core, and Visual Studio, ensuring organized and maintainable code by following programming best practices such as Object‑Oriented Programming (OOP), Domain‑Driven Design (DDD), design patterns, and Test‑Driven Development (TDD).
Collaborated within a cross‑functional team to streamline deployments by setting up an automated CI/CD pipeline in Azure DevOps to build and deploy the app, using Agile methodology, the full SDLC, and tools like Git for version control and Jira for task tracking, which improved team efficiency and delivery cadence.
Engineered a responsive front‑end UI using JavaScript, TypeScript, React, HTML, and Tailwind CSS, delivering a smooth single‑page application experience that adapts flawlessly across devices.
Orchestrated a retrieval‑augmented generation (RAG) system with PostgreSQL and OpenAI’s LLM, integrating vector similarity search on stored data and prompt engineering to provide personalized financial advice in real time.
Designed a normalized SQL schema using ER and relational modeling to optimize query performance and data integrity for the Pharmacy Management System.
Developed an ASP.NET Core (.NET 8) RESTful API with a database‑first approach, incorporating comprehensive integration tests for reliable scalability and streamlined debugging.
Documented all endpoints with Swagger/OpenAPI and Postman, migrated PostgreSQL to Azure via Azure Database Migration Service, and automated deployment through GitHub Actions to deliver a highly available, cloud‑native backend.
Built a decentralized content sharing application with a React front end and Node.js verifier server, enabling users to upload and register files on IPFS and interact with smart contracts on the Polygon Amoy Testnet.
Integrated Privado ID for off‑chain credential issuance and Metamask‑based wallet authentication, using QR‑code scanning to grant verified access to the DApp’s content management features.
Wrote and deployed Solidity smart contracts on the Polygon Amoy Testnet to handle content registration, metadata management, and ownership transfers, and implemented automated migration and testing scripts via Hardhat for reliable contract lifecycle management.
Stay tuned for my next exciting project! I'm working on something new and innovative.
Zeng, Q., Bashir, A. H., & Nait-Abdesselam, F. (2025). UAVIDS-2025: A Benchmark Dataset for Intrusion Detection in UAV Networks Using Machine Learning Techniques. Accepted to the 2025 IEEE Conference on Communications and Network Security (CNS), Avignon, France; to be published in IEEE Xplore and presented August 2025.
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