Groceries Misplacement Detection via Object Recognition

I'm seeking a skilled data scientist or machine learning engineer to develop an object detection model that can identify misplaced grocery items on retail shelves. The model should be based on YOLOv10 and trained using the SKU110k dataset. I also need the following requirements:

Key Requirements:
- Develop a camera-based object detection system.
- The model needs to recognize grocery items based on their size, color, and shape.
- Prior experience with computer vision and object detection is essential.
-The model should be fully deployed using Docker and Jenkins for seamless integration and CI/CD pipelines.
-The deployment should be on EC2 instances.
-I need detailed graphs showing the model’s performance.
-When I upload a video, the model should analyze it and provide an alert identifying any misplaced items.

Ideal Skills:
- Proficiency in Python and machine learning libraries such as TensorFlow or PyTorch.
- Experience with camera-based detection systems.
- Strong understanding of object recognition based on size, color, and shape.
- Devops
- Mlops expert

A successful project will enhance the efficiency of our retail operations and improve the shopping experience for our customers.
Also I really want this done in a day thats very crucial