Bengaluru, Karnataka
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:
AI/ML Engineer with focus on traditional ML (deep learning, CV) - Specialist
Tell us about one ML/DL/Computer Vision use case you have worked on in a real-world setting (industrial, product or enterprise)
For one model you implemented (ML/DL/CV), explain: which algorithm/architecture you chose and why, alternatives you considered and key trade-offs
Describe a situation where your model performed differently in production than in development. What did you do differently afterward in your approach?
Have you created reusable ML component, pipeline or standards across teams? What problem were you solving and what was adopted?
For one of the use cases above, share:
- One specific challenge in the solution flow and how you addressed it
- 2–3 concrete ways you validated that the solution worked as expected (metrics, checks or feedback loops)
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