Backend engineer to build a CI regression service for AI coding agents (.NET / Python / Azure / AWS)
Budget: $25.0 - $45.0
HOURLY / PART_TIME
⭐ 0.00 (0)
United States
c#, postgresql, python, api, .net-framework, windows-azure, amazon-web-services
Preferred qualifications
- Experience: Intermediate
**Summary**
I am looking into building a backend service that runs a team's AI coding agent against their own private repository in isolated sandboxes, grades each run with hidden tests, and reports whether a prompt or model change made the agent better or worse. It is API-first: a REST API with webhooks, a GitHub Action, and a CLI.
The work includes several components: the control-plane API and job runner, the GitHub App and check, run comparison and scheduling, and hardening and deployment with Terraform.
**Example of the finished product in use**
In one sentence: CI for an AI coding agent. The team has a test suite for their code; we give them one for the agent that writes their code. We do not build the agent or the model; we run someone else's agent in isolation and measure it.
A team ships a bot that takes a GitHub issue and opens a pull request. With our service:
1. They register their repository, their agent (a Docker image and a command, using their own model key), and a set of tasks: past issues, each with a hidden test.
2. A developer changes the agent's prompt and opens a pull request.
3. We run the agent on every task in its own sandbox and run each hidden test.
4. The PR gets a check: pass rate against main, which tasks regressed, which were fixed, and a link to a JSON diff.
5. The developer reads the diff, fixes the prompt, and pushes.
**Constraints**
- Language: C# (.NET, ASP.NET Core), Python (FastAPI), or TypeScript (Node).
- Cloud: Azure or AWS. Managed containers, a queue, Postgres, object storage, and the cloud's secret store (Container Apps and Service Bus, or Fargate and SQS, with their equivalents). Terraform either way.
- Database: PostgreSQL for suites, runs, and results. Object storage for trajectories and artifacts.
- Frontend is minimal. The GitHub check and the JSON are the interface in the first version.
**What exists today**
Architecture notes, the API resource list, and a task format. No code.
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