Develop, test, fine-tune, and optimize computer vision and object detection models using video and image datasets
Support the deployment of ML models in production or near-production environments
Analyze and process large-scale visual datasets, including annotation workflows and video sample preparation
Build new models and adapt pre-trained models to support operational and safety-focused use cases
Evaluate technical feasibility and prioritize AI/ML use cases according to business timelines and delivery goals
Collaborate with cross-functional stakeholders including Infrastructure, Operations, AI Engineering, and business teams to translate operational requirements into technical solutions
Contribute to the development of real-time detection and alerting capabilities using camera-based systems
Support rapid prototyping and MVP delivery initiatives within fast-paced environments

Advanced English communication skills
Strong hands-on experience with Computer Vision and Object Detection frameworks such as YOLO, TensorFlow, PyTorch, or similar technologies
Experience training, fine-tuning, validating, and deploying Machine Learning models
Background working with image and video data pipelines
Familiarity with annotation tools, labeling workflows, and visual dataset management
Experience working with cloud-based Machine Learning infrastructure, preferably within Azure environments
Strong understanding of ML engineering best practices and scalable deployment approaches
Ability to work independently while collaborating effectively within cross-functional teams

Computer Vision, Machine Learning Engineer

Key skills