Pandora is the World’s largest jewellery brand, known for affordable luxury, innovative design and our high-quality craftsmanship. Founded in 1982 in Copenhagen, Denmark, by Per Enevoldsen and his wife Winnie, Pandora started as a small jewelry shop and has since grown into the globally recognized brand it is today.
At Pandora, we are transforming the retail experience by leveraging our vast data combined with cutting-edge technology. Our mission is to provide an exceptional shopping experience to our customers by leveraging data-driven insights and innovative solutions across our business. We are looking for a talented MLOps Engineer to join our dynamic team and help us shape the future of retail.

JOB SUMMARY:

As an MLOps Engineer at Pandora, you will play a play a critical role in deploying, monitoring, and optimizing machine learning models in production environments. The ideal candidate will bridge the gap between our AI Team and our Data Platform engineers, ensuring the seamless integration of ML models into scalable and reliable systems. In this role you will design and implement robust MLOps pipelines, automate model training and deployment processes, and monitor the performance of ML systems, including systems for GenAI. Your expertise in cloud platforms, containerization, and CI/CD tools will drive the operational efficiency of our AI and machine learning initiatives. We encourage creative problem-solving, continuous learning, and work-life balance in a supportive environment.

QUALIFICATIONS:

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field.
• Experience:
• Solid experience as a MLOps Engineer, Machine Learning Engineer, DevOps Engineer, or similar role.
• Experience in the retail industry or e-commerce is highly desirable.
• Technical Skills:
• Strong experience with Infrastructure as Code frameworks and languages (Terraform, Bicep or ARM)
• Strong programming skills in Python and experience with ML frameworks (TensorFlow, PyTorch, etc.).
• Hands-on experience with containerization (Docker) and orchestration tools (Kubernetes).
• Proficiency in CI/CD tools and cloud platforms (AWS, Azure, or Google Cloud).
• Knowledge of model monitoring and evaluation metrics.
• Familiarity with version control systems, such as Git, and model versioning tools like MLflow or DVC.
• Experience with Generative AI product deployment is desirable.
• Experience with big data using Databricks, Snowflake, Apache Spark or Hadoop is desirable.
• System level architecture understanding including scaling, MLOps, model/data monitoring, and ensuring a deterministic pipeline.
• Soft Skills:
• Strong problem-solving skills with the ability to work independently and collaboratively in a fast-paced environment.
• Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
• A proactive attitude and a passion for continuous learning and innovation.

• Design, develop and maintain end-to-end MLOps pipelines for model deployment, monitoring, maintenance, and scalability.
• Automate the retraining, testing, and validation processes for ML models.
• Collaborate with cross-functional teams, including data science, software engineering and DevOps, to integrate ML models into production systems.
• Monitor model performance, diagnose issues, and implement improvements.
• Ensure scalability, reliability, and compliance of ML systems in production.
• Optimize infrastructure costs while maintaining high system performance.
• Stay up-to-date with the latest developments in MLOps, machine learning and AI, and apply new techniques to improve existing models and processes.