Auction Technology Group (ATG) is transforming the global auction industry by modernizing it through innovative online auction technologies. As they scale their AI/ML capabilities across search, recommendations, and computer vision, they need robust MLOps infrastructure to support rapid experimentation, reliable deployment, and continuous improvement.
Requirements
- BSc or MSc in Computer Science, Data Science, Engineering, or equivalent practical experience
- 3+ years building and maintaining ML pipelines and infrastructure in production environments
- Strong Python programming with software engineering best practices (testing, code review, documentation)
- Proven experience with MLOps platforms (MLflow, Kubeflow, Airflow, or similar)
- Hands-on experience with Docker, Kubernetes, and CI/CD tools
- Experience with cloud platforms (AWS, Azure, or GCP) and infrastructure-as-code (Terraform, CloudFormation)
- Familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn) and model serving frameworks
Responsibilities
- Design, build, and maintain end-to-end MLOps pipelines supporting the complete model lifecycle, from development and training to deployment, monitoring, and retraining
- Deploy and operationalize ML models for search ranking, recommendations, computer vision, and NLP in production, ensuring high availability and performance at scale
- Implement containerization (Docker) and orchestration (Kubernetes) strategies for scalable model serving across multiple environments
- Develop comprehensive monitoring systems to track model performance, data drift, and system health, with automated retraining pipelines triggered by performance degradation
- Build CI/CD pipelines tailored for ML workflows, including automated testing, validation, and deployment
- Establish model versioning, experiment tracking, and registry practices using MLflow or similar platforms
- Create standardized, reusable ML workflows and self-service tools that enable efficient model development and deployment
Other
- BSc or MSc in Computer Science, Data Science, Engineering, or equivalent practical experience
- 3+ years building and maintaining ML pipelines and infrastructure in production environments
- Strong problem-solving abilities and collaborative mindset for cross-functional teamwork
