Computer Vision Engineer — Edge AI (Manufacturing Safety)

Location: Remote

What This Role Is

Build and deploy real-time computer vision systems for factory safety.
You’ll take models from training → edge deployment → live factory use — and make them work reliably 24/7.

What You’ll Do

• Train & fine-tune object detection models (YOLO) for:
• PPE detection
• Zone intrusion
• Vehicle–pedestrian proximity
• Behavioral safety violations
• Integrate pose estimation for ergonomics & fall detection
• Optimize models for edge inference (ONNX, TensorRT on NVIDIA Jetson)
• Build real-time video pipelines (RTSP → inference → event output via MQTT)
• Implement privacy controls (face blurring, anonymization)
• Own dataset lifecycle: collection, labeling, augmentation, versioning
• Deploy on-site, tune per camera/environment, and iterate from real-world feedback
• Own full lifecycle: data → model → deployment → monitoring

Must Have

• Proven deployment of YOLO models to production (not just experiments)
• Experience with NVIDIA Jetson + TensorRT optimization
• Real-time video pipeline experience (RTSP streams, continuous inference)
• Strong Python + PyTorch + OpenCV
• Solid grasp of precision/recall/mAP and debugging model failures
• Experience managing datasets (CVAT, Roboflow, etc.)
• Ability to ship working systems independently

Strong Plus

• Pose estimation (MediaPipe, RTMPose)
• Object tracking (DeepSORT, ByteTrack)
• MQTT / edge-to-cloud / IoT systems
• Manufacturing or industrial environments
• Anomaly detection / segmentation use cases
• Docker for edge deployment

What Matters

You don’t just build models — you make them work for the real world.
Speed, reliability, and iteration matter more than research.

Job Type: Full-time

Pay: ₹500,000.00 - ₹1,000,000.00 per year

Experience:

• Computer vision: 5 years (Preferred)
• object detection model: 3 years (Preferred)

Work Location: Remote