Senior .NET Developer | Enterprise AI | SQL Server | Python | Open Source Contributor

I build backend systems, enterprise AI foundations, document-processing platforms, and database-driven business software using C#, ASP.NET Core, SQL Server, PostgreSQL, Redis, Docker, and modern AI/RAG architectures.

My current focus is on production-oriented AI backends that connect business documents, semantic search, retrieval-augmented generation, and reliable .NET-based APIs.

I actively contribute to the .NET and open-source ecosystem through small, focused, reviewable pull requests.

• dotnet/docs PR #54567: documented sizeof behavior for enum types in the C# language reference.
• dotnet/docs PR #54559: fixed an ASP.NET workload typo in the .NET microservices documentation.

• OrchardCore PR #19491: adds a safe breadcrumb back link to the content edit page using the existing local returnUrl flow.
• dotnet/aspnetcore PR #67481: clarifies ActionLink URL generation documentation for null protocol and host behavior.
• SQLAlchemy PR #13417: adds inline type annotations to the legacy sqlalchemy.ext.serializer extension.

A production-oriented backend foundation for enterprise document assistants, built with ASP.NET Core, Python FastAPI, Docker Compose, PostgreSQL, and Redis.

Highlights

• Multi-service architecture with .NET API and Python AI service
• Document upload and metadata management
• Text extraction, chunking, embedding, and semantic search foundations
• Swagger/OpenAPI documentation
• Health endpoints
• GitHub Actions CI, API tests, and Docker validation foundation
• Roadmap for RAG question answering, authentication, indexing, observability, and multi-tenancy

A modular .NET toolkit for enterprise AI applications, focused on clean abstractions, provider-independent design, and reusable AI backend foundations.

Highlights

• .NET 8 solution structure
• Provider-agnostic chat abstraction
• Test and CI foundation
• Minimal runnable console sample
• Roadmap for embeddings, vector stores, RAG, and document ingestion

A research-to-code project focused on concurrency, distributed systems, fairness, and wait-free synchronization concepts.

A computer vision project for Persian license plate recognition using object detection and character recognition workflows.

• Enterprise backend systems with C#, ASP.NET Core, REST APIs, and relational databases
• AI document assistants with upload, indexing, semantic search, and RAG workflows
• Provider-agnostic AI service abstractions for enterprise applications
• Multi-service systems using .NET, Python FastAPI, Docker, PostgreSQL, and Redis
• Database-heavy business applications, reporting systems, and workflow automation
• Research-oriented software around AI, ML, medical imaging, and recommender systems

Backend: C#, ASP.NET Core, REST APIs, SQL Server, PostgreSQL
AI / RAG: document processing, semantic search, retrieval-augmented generation, embeddings architecture
Infrastructure: Docker, Docker Compose, Redis, CI workflows, service-oriented architecture
Open Source: .NET documentation, ASP.NET Core, OrchardCore CMS
Research: machine learning, medical image analysis, AI model recommendation, software engineering
Leadership: Founder & CEO, software architecture, product-oriented engineering

I am actively improving my GitHub portfolio around three tracks:

1. Enterprise AI backend systems
2. Open-source contributions across the .NET and Python ecosystems
3. SQL Server, AI, and automation platforms with production-ready engineering practices

• GitHub: @mahdiaghtaee
• Email: [email protected]