Job description:
We are hiring a Machine Learning Engineer / Python Developer to build a real-time hanger detection and counting system for the garment industry. The ideal candidate will use computer vision and deep learning to detect and count hangers from live camera feeds, ensuring accuracy and performance in a real production environment.
Key Responsibilities:
- Develop and guide our team on object detection models using YOLOv5/v8, TensorFlow, or PyTorch to identify hangers on a moving production line.
- Process live camera feeds using OpenCV and stream via RTSP or USB/webcams.
- Build a complete detection and counting software solution tailored for the garment industry.
- Ensure accurate detection and prevent double-counting using object tracking algorithms (e.g., SORT, DeepSORT).
- Prepare and label custom datasets using tools like LabelImg or Roboflow.
- Optimize the model to run efficiently on industrial PCs or edge devices like Jetson Nano or Raspberry Pi.
- Collaborate with hardware/software engineers for system integration and deployment.
- Build a basic dashboard or interface (optional) using Flask/FastAPI for internal use.
Mandatory Skills & Requirements:
- Strong Python programming skills with OpenCV experience Knowledge of YOLOv5/v8 or equivalent object detection frameworks.
- Hands-on experience with deep learning frameworks like PyTorch or TensorFlow.
- Experience with live video feed processing and stream handling.
- Understanding of object tracking to avoid miscounts.
- Familiarity with data annotation tools like LabelImg or Roboflow.
- Good problem-solving and debugging skills.
- Freshers do not apply.
Good to Have:
- Degree in Computer Science, Artificial Intelligence, or related field.
- Experience deploying ML models on Jetson Nano, Raspberry Pi, or industrial PCs.
- Familiarity with Flask/FastAPI for lightweight APIs.
- Experience using Docker and Git for version control and deployment (optional but helpful).
Job Types: Full-time, Permanent
Pay: ₹600,000.00 per year
Benefits:
- Work from home
Work Location: In person