Qoves Inc

QOVES builds a data-heavy analysis platform for paying customers. We need one engineer to own customer-facing bugs from report to root cause. You will work every day with our delivery and QA team in the Philippines. They find problems in customer orders. You find out why those problems happen and fix them so they stop coming back.

The work:

• A bug arrives as an order ID and a short description. You trace it across several services and background jobs until you find where it broke.
• You fix the code, or the data when the data is the cause, then ship the fix and confirm it worked.
• When a job gets stuck, you repair or re-run it, then work out why it stuck.
• When the same failure keeps returning, you fix its cause once.
• You write short, clear updates for people who are not engineers.

Most problems are jobs that stall or retry badly, data that disagrees between two services, account and subscription bugs, failing emails, downloads and generated documents, and bugs in our internal admin tool.

What you need:

• 4 to 7 years of professional Python
• Django and Django REST Framework
• FastAPI and async Python
• PostgreSQL, including reading query plans and fixing data safely
• Background jobs and queues (Celery and Redis, or similar). Temporal or another workflow engine is a strong plus.
• Git, code review and pull requests
• Strong written English. Most of the job is writing.

Nice to have:

Stripe billing, AWS (S3, SQS), Docker, basic Kubernetes, Sentry and log-based debugging, and some image or ML pipeline exposure. You will not train models.

This role is not first-line customer support, because you do not answer customers. It is not model research or data science. It is not a pure feature developer job either.

Conditions:

remote, full-time, with at least 4 hours of overlap with the Philippines working day. Some tickets are urgent, and we need a fast response when a customer is blocked.

You will do well if you check a fix before you call it fixed, keep asking why until you reach the real cause, and stay calm when a customer is waiting.

About the role

We give you a product design and a brief. You turn it into backend software that analyses images and user input and hands our frontend team the structured data it needs.

Most of the code gets written by AI agents like Claude Code and Codex, with you directing them. That makes you responsible for every line they produce, including the wrong ones. Your value is knowing what to ask for and catching what the agent got wrong. If “the AI did it” sounds like a reasonable defence to you, this job will frustrate you.

What you will do

1. Read a design and work out what data each part of it needs.
2. Build the backend logic that produces that data, with AI coding agents doing most of the typing.
3. Set up open-source computer vision and AI models, run them on real images, and decide whether they are good enough.
4. Check your own output before anyone reviews it. Wrong values, invented APIs and messy code should stop with you.
5. Ship small, well-typed pull requests that get through code review.

You won’t have to come up with the product itself. We do that part, and your job is to make it work and keep it maintainable.

You will fit if you

1. Write clear, precise English in prompts, pull requests and messages. This is the first thing we screen for.
2. Use Claude Code, Codex or Cursor every day, and can show us how you break a task down for an agent.
3. Know enough computer vision, machine learning and data engineering to ask for the right thing and to recognise a wrong answer when you see one.
4. Are a solid Python engineer who understands typing and async, and who spots bad patterns in a diff quickly.
5. Look at the actual output every time. Code that runs proves very little.

Nice to have

1. Experience with OpenCV or similar image libraries.
2. Experience with FastAPI, workflow engines, or cloud storage.
3. Experience getting structured outputs from LLM APIs.
4. A side project where you got an open-source model running and evaluated it.

How to apply

Send your CV and answers to these 3 questions, 150 words maximum each:

1. Describe one time an AI coding agent gave you code that looked right but was wrong. How did you find out?
2. You must estimate the real-world size of an object from a single photo. What steps and checks would you ask an AI agent to build?
3. Paste one real prompt you recently gave an AI coding agent, and tell us what happened next.

Include a link to one piece of your work: a repository, pull request, write-up or demo.