Senior Machine Learning / Data Engineer

Senior Machine Learning / Data Engineer

• Education: Master degree in Computer Science, Data Engineering, Machine Learning, or a related field
• Experience:
• 3+ years of experience in data engineering, machine learning engineering, or a related role
• Experience in building and maintaining data pipelines for machine learning systems (data ingestion, preprocessing, validation, versioning)
• Experience supporting end-to-end ML workflows (data preparation, training, evaluation, deployment)
• Familiarity with cloud-based data platforms and infrastructure (data lakes, pipelines, orchestration, monitoring)
• Experience working with large, structured and unstructured datasets, including image data (computer vision pipelines is a plus)
• Experience contributing to data labeling, cleaning, and dataset quality control, including working with annotation tools and processes
• Skills: Strong programming skills (especially Python), solid understanding of data structures, data processing, and machine learning workflows
• Communication: Ability to collaborate effectively with data scientists and engineers and clearly communicate data-related challenges and solutions

Preferred attributes

• Hands-on mindset: Willingness to actively contribute to data labeling, cleaning, and dataset improvement
• Pragmatic: Focus on building robust, scalable, and maintainable data pipelines
• Detail-oriented: Strong focus on data quality, consistency, and reproducibility
• Adaptable: Comfortable working in fast-paced, evolving environments
• Experience working in international or cross-cultural environments is a plus
• Exposure to global teams, customers, or academic environments is preferred

Your tasks

• Design, build, and maintain scalable data pipelines for AI/ML systems
• Ensure data quality, consistency, and versioning across datasets and customers
• Support the full ML lifecycle, including data preparation, training, evaluation, and deployment workflows
• Actively contribute to data labeling, cleaning, and dataset validation, working closely with domain experts
• Develop tools and processes to improve labeling efficiency and data quality at scale
• Collaborate with data scientists and AI engineers to optimize model performance through better data
• Monitor and address data drift, dataset gaps, and customer-specific variability

Interested in shaping the future of textile quality assurance?

Apply now and join our mission to deliver excellence in every thread.

Selection process

1st round: Online technical assessment

2nd round: Interview via Teams

3rd round: HR discussion