AI Engineer - Computer Vision

About the role

Requirements

• ·       Develop and train Computer Vision models for image and video analysis.
• ·       Implement solutions for object detection, image classification, segmentation, and tracking.
• ·       Prepare and manage datasets, including data cleaning, labelling, and augmentation.
• ·       Optimize models for GPU performance, inference speed, and scalability.
• ·       Deploy AI models into production using APIs and containerized services.
• ·       Integrate Computer Vision models with broader AI and analytics platforms.
• ·       Collaborate with software engineers, data scientists, and product teams.
• ·       Maintain documentation and ensure code quality and reproducibility.
• Required Skills & Qualifications
• ·       Minimum 2 years of hands-on experience in AI / Machine Learning/Deep learning with a focus on Computer Vision.
• ·       Master’s degree in computer science, software Engineering, Artificial Intelligence, Engineering, or a related field.
• ·       Strong knowledge of Computer Vision concepts and algorithms.
• ·       Practical experience with CNN-based models and modern CV architectures.
• ·       Proficiency in Python and common ML/CV libraries (PyTorch or TensorFlow, OpenCV).
• ·       Experience with object detection and segmentation frameworks (e.g., YOLO, Detectron2).
• ·       Understanding of model evaluation, metrics, and performance optimization.
• ·       Experience deploying models in cloud or on-prem environments.
• ·       Familiarity with Docker and basic MLOps practices.
• ·       Excellent English communication skills (written and spoken).
• Nice to Have
• ·       Experience with video analytics or real-time inference.
• ·       Exposure to Vision Transformers or multimodal AI.
• ·       Experience with GPU-based training and inference.
• ·       Knowledge of cloud platforms (OCI, AWS, Azure, or GCP).
• ·       Background in domains such as healthcare, smart cities, or industrial AI.

Responsibilities

Skills

• Python
• PyTorch
• TensorFlow
• OpenCV
• CNN-based models
• YOLO
• Detectron2
• image classification
• object detection
• segmentation
• model deployment
• Docker
• MLOps
• GPU optimization
• production-grade computer vision