Computer Vision Engineer — Detection, Tracking & LLMs

BBI; Dubai; On-site; AED 180,000 - 300,000; Full time

Job summary

BBI is seeking a skilled computer-vision ML engineer to develop and refine models for real-world projects in Dubai. You will work with PyTorch/TensorFlow, OpenCV, and cutting-edge architectures like YOLO and RF-DETR to deliver reliable detection, tracking and segmentation solutions.

You will curate datasets, fine-tune Vision-Language Models, and monitor performance with mAP, precision and recall while coordinating with annotation teams.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, ML or related field.
• 3-7 years of applied ML experience with a computer-vision focus.
• Strong Python with hands-on PyTorch or TensorFlow and OpenCV.
• Experience with detection, tracking and segmentation models (YOLO, RF-DETR).
• Experience fine-tuning Vision-Language Models and familiarity with experiment-tracking tools is an advantage.

Responsibilities

• Develop and fine-tune computer-vision models (detection, tracking, segmentation).
• Work hands-on with YOLO, RF-DETR and Vision-Language Models for project use cases.
• Curate, preprocess and augment image/video datasets, maintaining a feedback loop with the annotation team.
• Train, tune and evaluate models against metrics (mAP, precision, recall); track experiments via MLflow/Weights & Biases.
• Apply model-optimisation techniques (TensorRT, ONNX) with deployment constraints in mind.

Skills

Python

PyTorch/TensorFlow

OpenCV

Computer vision

Education

Bachelor's or Master's in CS/DS/ML

Tools

YOLO

RF-DETR

Vision-Language Models

MLflow/Weights&Biases

Job description

BBI is seeking a skilled computer-vision ML engineer to develop and refine models for real-world projects in Dubai. You will work with PyTorch/TensorFlow, OpenCV, and cutting-edge architectures like YOLO and RF-DETR to deliver reliable detection, tracking and segmentation solutions.

You will curate datasets, fine-tune Vision-Language Models, and monitor performance with mAP, precision and recall while coordinating with annotation teams.