Job Summary

Expectation for all engineer profiles

• Foundational AI/ML & Software Engineering
  • Strong grounding in ML fundamentals and software engineering, enabling translation of business problems into robust, scalable AI/ML solutions
  • Experience in designing, building and integrating production-grade systems using modern engineering practices (APIs, microservices, CI/CD, containerization)
  • Ability to bridge classical ML approaches with emerging GenAI paradigms, applying the right techniques to deliver reliable and maintainable solutions
  • Effectively leverage AI-assisted development tools (e.g., GitHub Copilot, Claude Code) to accelerate prototyping, improve engineering quality and enhance developer productivity
• Product Collaboration & Enablement
  • Ability to work effectively within agile product teams, collaborating in iterative cycles to refine requirements, validate hypotheses and deliver incremental AI/ML value
  • Ability to drive alignment independently across product, AI/ML engineering, platform and MLOps teams to achieve shared engineering outcomes
  • Strong capability in early-stage AI/ML solution development, including problem framing, feasibility assessment, rapid prototyping and iterative experimentation
  • Effective collaboration across geographically distributed teams (Denmark, India, Portugal), with strong cross-cultural awareness and communication

Key Responsibilities

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

Skill Requirements

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

Other Requirements

• AI Engineer

• • Specialist: 8-12+ years of experience in software engineering, data or analytics, with significant hands-on experience and demonstrated impact in AI/ML solution development, including 4-6+ years focused on AI/ML 

Candidates need to answer the following: