I’m looking for someone who can turn the open-source BiRefNet background-removal model into a production-ready RunPod Serverless endpoint. I will import your Git repository straight into a fresh Serverless GPU instance, so what I need from you is a self-contained, minimal Docker solution that boots instantly and never fetches weights at runtime. What the endpoint must do • Accept an image supplied either as a base64 string or as a direct URL • Run BiRefNet to remove the background • Return a base64-encoded PNG with transparency Technical specifics – Create handler.py, requirements.txt and Dockerfile. – Download and cache the BiRefNet weights during the image build; no network calls when the container wakes up. – Optimise the image for size; only essential packages should be included. – Ensure flash-boot performance so cold starts feel instantaneous. Deliverables 1. Git repository containing the Dockerfile, handler.py and requirements.txt. 2. Clear README explaining any environment variables and the exact payload/response structure. 3. Quick verification steps so I can run docker build, push, and see the endpoint work on RunPod without tweaks. If you’ve previously deployed Hugging Face models or worked with RunPod’s Serverless API, this should be a straightforward job. Let me know your estimated turnaround and any questions you might have.