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.

Competencies :

- 5+ years of experience in software engineering, data or analytics, with strong hands-on experience in AI/ML solution development, including 2- 4+ years focused on AI/ML.

- 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.