Senior .NET Developer | Enterprise Backend Systems | AI-enabled Applications | SQL Server

I design and build backend systems with C#, ASP.NET Core, SQL Server, PostgreSQL, Redis, and Docker. My current work focuses on document processing, semantic retrieval, and AI-provider integration without losing the reliability, security boundaries, and testability expected from enterprise software.

I use this profile to document real project work, architecture decisions, technical trade-offs, and open-source contributions. I prefer small, reviewable changes and accurate engineering documentation over inflated claims or demo-only features.

A local-first reference project for integrating persistent document retrieval into an ASP.NET Core backend.

Upload -> Persist metadata -> Extract -> Chunk -> Embed -> Persist vectors -> Search -> Answer with sources

The current implementation includes:

• ASP.NET Core for public APIs, local extraction, chunking, deterministic embeddings, semantic retrieval, and source-aware answers;
• PostgreSQL for document metadata and persistent pgvector-backed chunk embeddings;
• configuration-driven InMemory and Postgres semantic-index providers behind one interface;
• Python FastAPI as a health and indexing boundary for future Python-specific document or model integrations;
• Redis as infrastructure for future caching or background work;
• Docker Compose, Swagger, sample documents, and an end-to-end demo;
• .NET integration tests, retained coverage artifacts, FastAPI endpoint tests, Ruff validation, runtime Compose health checks, CodeQL, Dependency Review, Dependabot configuration, and CODEOWNERS;
• a CI persistence scenario that uploads and searches a document, restarts the API container, and verifies that retrieval still succeeds.

Processing is still synchronous, the deterministic embedding model is intended for reproducible development rather than production retrieval quality, and access control is not implemented. The next engineering steps are background indexing, authentication and document authorization, audit logging, observability, and retrieval evaluation.

Repository · Engineering case study · Architecture · Roadmap

I contribute focused changes to established projects and work through maintainer feedback rather than treating pull requests as one-time submissions.

• dotnet/aspnetcore #67481 — clarified ActionLink URL-generation documentation for null protocol and host behavior.
• dotnet/docs #54567 — documented sizeof behavior for enum types in the C# language reference.
• dotnet/docs #54559 — corrected an ASP.NET workload typo in the .NET microservices documentation.

An early .NET foundation for provider-independent AI contracts, with a runnable sample, tests, and CI.

An exploratory research-to-code project about randomized locking, contention, fairness, and the limits of reproducing algorithmic progress guarantees in Python.

An archived computer-vision study project for Persian license-plate and character recognition.

Backend: C#, ASP.NET Core, REST APIs, SQL Server, PostgreSQL
AI systems: document processing, semantic search, RAG, provider abstraction
Infrastructure: Docker, Docker Compose, Redis, CI, service-oriented architecture
Engineering: API contracts, database workflows, architecture documentation, integration testing
Open source: .NET, ASP.NET Core, technical documentation, focused maintenance contributions

• deciding when a .NET application should call a separate Python service and when a modular application is simpler;
• designing a configurable semantic-index abstraction with in-memory and pgvector implementations;
• verifying persistence through container restart tests rather than documentation claims;
• building test paths that do not depend on external AI providers;
• designing SQL-heavy workflows, reporting systems, and enterprise integrations;
• responding to maintainer review and improving code without expanding scope unnecessarily.

• GitHub: @mahdiaghtaee