Role & responsibilities
Artificial Intelligence and Machine Learning Specialist.
Preferred candidate profile
Responsibilities:
- Contribute to the development and deployment of ML, deep learning and computer vision solutions for industrial use cases across the Vestas value chain
- Build and enhance ML/DL/CV solution components, including pipelines and inference workflows, while developing and optimizing deep learning and computer vision models
- Integrate ML, deep learning and computer vision capabilities into enterprise applications and edge/cloud systems using APIs, microservices and containerized environments
- Collaborate with solution team to deliver reliable ML/DL/CV solutions end-to-end, contributing to development, testing and deployment while following engineering and MLOps standards
- Continuously learn and apply best practices in traditional ML, deep learning and computer vision, including advancements in model architectures, training techniques and deployment optimization
Competencies:
- Traditional ML & Deep Learning Systems Engineering
- Ability to contribute to building scalable and reliable ML, deep learning and computer vision systems with focus on performance, robustness, data integrity and maintainability
- Understanding of standard design patterns and engineering practices for training, evaluating and deploying ML/DL models (including distributed training and efficient inference)
- Familiarity with deploying and integrating ML/CV solutions into production environments across cloud and edge systems
- ML, Deep Learning & Computer Vision Solution Development
- Hands-on capability in developing ML, deep learning and computer vision solutions for structured data, image/video data and industrial use cases
- Working knowledge of computer vision techniques such as object detection, image classification, segmentation and video analysis, along with deep learning architectures (CNNs, vision transformers, transfer learning and model optimization)
- Working knowledge of techniques such as feature engineering, model selection, hyperparameter tuning, transfer learning and model optimization (e.g., pruning, quantization)
- Ability to implement end-to-end ML workflows, including data preprocessing, model development, evaluation and deployment, with support for human-in-the-loop and decision-support systems