Summary

We are looking for a
Systems-First Computer Vision Engineer
who specializes in the "Last Mile" of AI: taking a model and making it run continuously, reliably, and instantly on live video feeds. This role is
not
about training models in a notebook; it is about building the high-performance highways (Pipelines) that allow Vision AI to run in the real world. You will architect robust streaming architectures using GStreamer/RTSP and optimize inference for ultra-low latency on Edge and Cloud environments.

Key Responsibilities
- Architect Streaming Pipelines:
Design and implement robust, real-time video ingestion pipelines handling multiple RTSP streams using tools like
GStreamer, FFmpeg, and WebRTC
.
- Inference Integration:
Take trained models from the ML team and integrate them into production pipelines. Your goal is to ensure the model runs stable, fast, and without memory leaks.
- Latency Optimization:
Obsess over milliseconds. Optimize data processing pipelines to ensure low-latency inference on both Edge devices (NVIDIA Jetson) and Cloud servers.
- Fault Tolerance:




Build "Crash-Proof" systems. Ensure that if a camera goes offline or a frame is dropped, the system recovers gracefully without manual intervention.
- Framework Evolution:
Maintain and evolve our proprietary vision framework by writing modular, reusable, and productive Python code/libraries.
- Performance Engineering:
Diagnose bottlenecks in the system—whether it's CPU, GPU, or Network—and implement architectural fixes.

Skills & Requirements
- Video Engineering Mastery:
Deep expertise in video streaming protocols (
RTSP, WebRTC, FastRTC
) and processing tools (
FFmpeg, GStreamer
). You know how to handle frame buffers, decoding, and encoding efficiently.
- Core Vision Stack:
extensive experience with
OpenCV
and Image Processing fundamentals. You understand geometry, color spaces, and pixel-level manipulation.
- Production Python:
Strong experience writing
fault-tolerant, multi-threaded/async co

📌 AI Lead (India)
🏢 KoiReader Technologies
📍 India