This employer is no longer accepting CVs for this job, it has been deactivated by the company's administrator.

AI Engineer

AI (Computer Vision Engineer) – Job Description

About the Role:
We're looking for a hands-on Computer Vision Engineer to join our fast-paced AI team. You'll own the full lifecycle of CV models — from training to edge deployment — in a high-ownership, move-fast culture.

Key Responsibilities:

Design, train, and evaluate computer vision models (detection, segmentation, classification)
Work with YOLO and similar architectures for real-time object detection tasks
Apply data augmentation strategies and preprocessing pipelines to improve model robustness
Convert and optimize models for edge deployment
Deploy and benchmark CV models on edge devices
Apply quantization, pruning, and distillation to create lightweight, production-ready models
Collaborate with hardware and software teams to integrate models into end-to-end systems

Responsibilities

Design, train, and deploy computer vision models, with a focus on YOLO variants, ensuring high accuracy and efficiency.
Implement data preprocessing and augmentation methods to improve model generalization and robustness.
Convert trained models into lightweight formats optimized for deployment on edge hardware platforms.
Develop and refine neural network architectures and machine learning algorithms aligned with project goals.
Utilize Python, PyTorch, and OpenCV to build and maintain AI pipelines and integrate models into applications.
Monitor model performance and implement improvements in model training procedures and deployment workflows.
Research and incorporate advanced techniques in deep learning and computer vision to keep solutions state-of-the-art.
Collaborate with cross-functional teams to gather requirements and translate them into technical AI implementations.
Maintain documentation of model development processes, training datasets, and deployment configurations.
Stay updated on the latest trends in multi-object tracking and MLOps to enhance operational efficiency.
Own projects from inception to deployment, demonstrating a proactive and ownership-driven approach.
Troubleshoot and resolve issues arising during model training, evaluation, and deployment phases.