AI Engineer

Responsibilities

• Develop and optimise computer vision algorithms for video analytics applications.

• Work on object detection, classification, segmentation, object tracking, and event detection.

• Develop multi-frame video analysis logic using tracking, trajectory, direction, speed, ROI, and temporal information.

• Support applications such as intrusion detection, loitering detection, PPE detection, falling object detection, people counting, vehicle monitoring, and other abnormal event detection.

• Train, fine-tune, test, and evaluate computer vision models using real project datasets.

• Analyse false positives and false negatives and improve model accuracy through model tuning, data improvement, tracking, or post-processing.

• Develop reusable computer vision modules for detection, tracking, event analysis, and alarm triggering.

• Work closely with software and deployment engineers to integrate algorithms into production systems.

  Preferred Skills

• Familiarity with ONNX or TensorRT.
• Experience with VLM-based visual analysis or verification is an advantage.
• Experience with CCTV, security, smart city, industrial vision, factory automation, or traffic analytics applications.
• Experience with optical flow, background subtraction, segmentation, or pose estimation.
• Experience with ByteTrack, DeepSORT,
  BoT-SORT, ReID

  , or other tracking algorithms.

  Requirements
• Good understanding of image processing and multi-frame video analysis.Hands-on experience with OpenCV and PyTorch or similar deep learning frameworks.
• Knowledge of object tracking and video analytics.
• Experience in training, testing, or improving computer vision models.
• Experience with object detection models such as YOLO, RT-DETR, Faster R-CNN, or equivalent.
• Good programming skills in Python.
• Bachelor's Degree in Computer Science, Computer Engineering, Electrical/Electronic Engineering, AI, or related field.
• Able to write clean, modular, and maintainable code.