Sr C# Developer

The client runs a set of Java microservices and wants to convert them to C# following their in-house C# patterns and conventions. The plan is to run the Java code through an AI-assisted conversion pipeline to get a first pass (they expect roughly 50 percent, not a finished product), then have senior developers review, correct, and complete the conversion so the C# services are production ready. The client will supply the target C# patterns, so the developer needs to read both languages fluently and translate idioms, not just syntax.
What you will do

- Convert Java microservices to C# / .NET, using AI-generated first drafts as a starting point and finishing them by hand to the clients coding patterns.

- Review AI-converted output for correctness, missed edge cases, hallucinated APIs, and behavior drift between the Java original and the C# result.

- Map Java constructs and libraries to their .NET equivalents (Spring to ASP.NET Core, Maven/Gradle to NuGet, JPA/Hibernate to Entity Framework, etc.).

- Preserve service contracts: REST endpoints, message formats, error handling, logging, and config behave the same after conversion.

- Write or port unit and integration tests to prove functional parity before and after migration.

- Help shape the conversion approach: prompt templates, pattern libraries, and review checklists so the process scales across many services without one-off effort.

- Work directly with client engineers, explain trade-offs plainly, and flag where a straight conversion is the wrong call.
Must have

- Deep Java: 8+ years building production Java microservices (Spring Boot or similar). Can read unfamiliar Java code and explain what it does and why.

- Strong C# / .NET: 5+ years hands-on with C# and .NET Core / .NET 6+, ASP.NET Core Web APIs, dependency injection, async patterns.

- Real migration experience: Has personally converted or re-platformed code between languages or frameworks, not just maintained the result.

- Microservices and REST: API design, service-to-service communication, containerization (Docker), CI/CD.

- Testing discipline: Comfortable proving parity with automated tests, not visual inspection.

- Communication: Works independently, translates technical detail for business stakeholders, gives direct feedback.
Nice to have

- Experience using AI coding tools (GitHub Copilot, Claude, ChatGPT, or similar) for code conversion or large refactors, and an honest view of where they fail.

- Familiarity with LLM-assisted development workflows: prompt design, output validation, agentic coding tools.

- Kubernetes, AWS or Azure, API gateways (Kong).

- Exposure to MySQL and SQL Server.

- Exposure to Python (helpful for tooling around the conversion pipeline).