Data Engineer (ML Platform & MLOps)

Data Engineer (ML Platform & MLOps) Employment Type: Full-time (40 hours/week) Duration: Long-term / Permanent Language Requirements: English (minimum B2 level), German is a plus Interview Process: 2–3 interview rounds About the Role We are looking for an experienced Data Engineer to build and scale the core data, machine learning, and deployment infrastructure powering an innovative AI-driven diagnostics platform. In this high-impact role, you will be responsible for creating scalable, reproducible, and traceable data and ML systems that support the entire lifecycle of clinical AI development—from data ingestion and research to model training, validation, deployment, and monitoring. You will work closely with Software Engineers, Machine Learning Engineers, and clinical stakeholders to ensure the integrity, quality, and regulatory compliance of critical healthcare data. This position combines Data Engineering, MLOps, and ML Platform Engineering within a highly regulated healthcare environment. Responsibilities Design and build robust data ingestion pipelines for large-scale multimodal datasets, including gigapixel pathology images, genomic data, and clinical metadata Collaborate with Software Engineers to develop testing frameworks, logging systems, and quality controls that ensure complete, accurate, and reliable data processing Build and maintain scalable data platforms supporting both structured and unstructured data workloads Develop ML infrastructure that enables reproducible model training, evaluation, deployment, monitoring, and lifecycle management Ensure data quality, consistency, lineage, and traceability throughout the entire machine learning workflow Implement compliant frameworks for: Dataset access management Model versioning Experiment tracking Auditability and governance Support regulatory requirements aligned with FDA, IVDR, and other healthcare standards Collaborate with Machine Learning Engineers to improve image processing workflows and data preparation pipelines Develop containerized, production-grade services for model inference and deployment across on-premises, cloud-based, and OEM-integrated environments Continuously improve platform reliability, scalability, and operational efficiency Required Qualifications 4+ years of professional experience in Data Engineering, ML Engineering, Platform Engineering, or related fields Proven experience building scalable pipelines for large-scale structured and unstructured datasets Strong experience with Infrastructure-as-Code (IaC) tools such as Terraform, CloudFormation, or similar technologies Experience designing and operating distributed or high-performance data processing systems Strong understanding of: Data modeling Data warehousing Data lineage and governance Distributed computing MLOps best practices Experiment tracking and model lifecycle management Minimum 2 years of experience developing data or machine learning systems in production environments Experience working in regulated industries such as healthcare, medical devices, life sciences, fintech, or other highly controlled environments Understanding of reproducibility, auditability, traceability, and compliance requirements in data-driven systems Strong Python programming skills Familiarity with at least one deep learning framework such as PyTorch or TensorFlow Excellent communication skills in English and ability to collaborate across technical and non-technical teams Valid work authorization within the European Union or Switzerland Willingness and ability to travel to Tunisia when required Preferred Qualifications Experience with healthcare, clinical, pathology, or biomedical data platforms Exposure to regulatory standards such as: ISO 27001 ISO 13485 FDA regulations IVDR requirements Experience with Software as a Medical Device (SaMD) environments Knowledge of clinical integrations and healthcare interoperability standards Experience supporting machine learning systems in production healthcare environments Familiarity with containerization technologies, Kubernetes, and cloud-native architectures Experience building secure and compliant ML platforms ⚠️ Important: Candidates who attach a CV to their application will be prioritized.