AI ML Specialist

Role Overview

We are seeking an Artificial Intelligence and Machine Learning Specialist to contribute to the creation and implementation of machine learning, deep learning, and computer vision solutions for industrial applications within the Vestas value chain.

Key Responsibilities

• Develop and deploy ML, deep learning, and computer vision systems designed for industrial use cases.
• Create and improve ML/DL/CV solution components including pipelines and inference workflows, optimizing models for performance.
• Integrate ML, deep learning, and computer vision functionalities into enterprise applications and edge/cloud platforms through APIs, microservices, and containerization.
• Collaborate with cross-functional teams to deliver robust end-to-end ML/DL/CV solutions, encompassing development, testing, and deployment following engineering and MLOps standards.
• Stay updated with advancements in ML, deep learning, and computer vision methodologies, and apply best practices in model architectures and deployment techniques.

Required Competencies

• Experience in traditional machine learning and deep learning systems engineering.
• Capability to design scalable and reliable ML, deep learning, and computer vision systems emphasizing performance, robustness, data quality, and maintainability.
• Understanding of design patterns and engineering best practices for training, evaluation, and deployment of ML/DL models, including distributed training and efficient inference techniques.
• Familiarity with production-level deployment and integration of ML and computer vision solutions across cloud and edge infrastructures.
• Practical experience developing ML, deep learning, and computer vision solutions for structured data, images, videos, and industrial use cases.
• Knowledge of computer vision methods such as object detection, image classification, segmentation, and video analysis; proficiency with deep learning architectures like CNNs, vision transformers, transfer learning, and model optimization.
• Expertise in feature engineering, model selection, hyperparameter tuning, transfer learning, and optimization methods including pruning and quantization.
• Ability to implement end-to-end ML workflows including data preprocessing, model creation, assessment, deployment, and support for human-in-the-loop and decision-support systems.

Eligibility

Applicants should be graduates in any field.