Analyze C# codebases for issues related to asynchronous programming, API architecture, database interaction, LINQ usage, object-oriented design, and backend engineering practices
Assess solutions for adherence to prompt requirements, .NET best practices, coding standards, and production-quality implementation patterns
Identify methodological errors, flawed logic, performance bottlenecks, incorrect assumptions, concurrency issues, and architectural weaknesses in AI-generated code
Write high-quality technical explanations and reference implementations demonstrating idiomatic C# and modern .NET engineering practices
Evaluate and compare multiple AI-generated responses based on correctness, reasoning clarity, maintainability, readability, and implementation quality
Review backend architectures involving ASP.NET Core, WebAPIs, dependency injection, database access layers, async workflows, and service-oriented design patterns
Analyze SQL queries, ORM usage, entity modeling, and data-access patterns using technologies such as EF Core and Dapper
Support AI model improvement through annotation workflows, engineering evaluations, response ranking tasks, technical reviews, and structured feedback generation
Contribute to AI training datasets that improve the reasoning, coding accuracy, debugging ability, and backend engineering capabilities of frontier AI systems
Requirements
Education: Bachelor s degree or higher in Computer Science, Software Engineering, or a related technical field; equivalent professional experience may also qualify
Minimum strong years of professional experience developing software using C#
Strong proficiency with the .NET ecosystem including ASP.NET Core, WebAPIs, async/await, LINQ, dependency injection, and backend service development
Experience working with relational databases, SQL query optimization, and modern data-access patterns using SQL Server and/or PostgreSQL
Hands-on experience with ORMs and data-access technologies such as EF Core and/or Dapper
Familiarity with modern engineering workflows and tooling including Git, CI/CD pipelines, Docker, testing frameworks, and deployment processes
Strong understanding of object-oriented programming, backend architecture principles, asynchronous workflows, and API design
Ability to evaluate code for correctness, maintainability, performance optimization, readability, and architectural quality
Strong analytical thinking and debugging skills with the ability to identify subtle technical and logical issues in AI-generated code
Significant experience using AI coding assistants or LLM tools such as ChatGPT, Gemini, Claude, Copilot, or similar platforms for coding, debugging, and code review preferred