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We are expanding our team with a Data & ML Infrastructure Engineer who will take ownership of the platform that enables data ingestion, processing, storage, and machine learning workflows across multiple products. Operating at the intersection of data science, software engineering, and cloud infrastructure, you will build the foundations and ensure that our ML systems are scalable, reproducible, and production ready. In this role, you will design, implement, and continuously improve a cloud-agnostic data and ML platform that supports multiple products and teams. You will be responsible for platform reliability, scalability, and cost-efficiency, while providing data scientists and ML engineers with the tools and infrastructure to efficiently develop, train, validate, and deploy machine learning models. You will serve as a technical bridge between the data science and IT/infrastructure teams, translating requirements into robust and maintainable systems. This role includes ownership of an existing vendor-delivered platform and its evolution into a fully automated, reproducible, and multi-product ML platform. Responsibilities: Design, implement, and maintain the data and ML platform infrastructure, including systems for data ingestion, storage, processing, and training. Ensure high availability, reliability, and uptime of the platform, as it is critical to the business value proposition. Maintain and evolve vendor-delivered platform components and ensure long-term maintainability and independence. Build and maintain data workflows, including tools for dataset versioning, experiment tracking, and model lifecycle management across multiple products. Enforce DevOps / MLOps practices, including CI/CD pipelines, Infrastructure-as-Code, and automated workflows. Develop cloud-agnostic and containerized solutions that can run across public and private cloud environments within EU constraints. Optimize data storage, lifecycle policies, and cost efficiency, including tiered storage and retention strategies. , Prometheus, Grafana) for pipelines, infrastructure, and resource usage. Ensure data governance, security, and compliance, including access control, audit logging, and anonymization requirements. Collaborate closely with data scientists, ML engineers, and IT teams to deliver infrastructure that meets operational needs. Manage resource and cost controls, including budgets, approvals, and tracking. Contribute to documentation, operational runbooks, and onboarding materials for the platform. Why Join Us? Join one of Europe’s most promising deep-tech scale-ups. Shape the future of a rapidly growing multidisciplinary R&D organization. Competitive salary and benefits package. Enjoy regular team activities and company events. Fresh team lunches provided3 times/ week. Qualifications: MSc or equivalent with 5+ years’ industry experience in Computer Science, Data Engineering, Machine Learning, or a related field. Strong experience with data platforms, cloud infrastructure, and distributed systems (AWS, GCP, Azure). , Terraform) and automated deployments. Experience with ML platforms and tools (MLflow, Feast, DVC, DataHub or equivalent). Experience with AWS Sagemaker or Google Vertex AI platforms. Experience with databases (MongoDB, PostgreSQL, InfluxDB or equivalent). Experience with message brokers and streaming systems (Kafka, RabbitMQ or equivalent). Hands-on experience with containerization (Docker/OCI) and orchestration (Kubernetes or similar). , GitLab CI). Programming skills in Python. Experience with monitoring and observability tools (Prometheus, Grafana). Understanding of DevOps and MLOps practices. Ability to work across teams and translate requirements into scalable infrastructure solutions. Nice to Have: Knowledge of data governance, IAM, and security frameworks.

Data & ML Infrastructure Engineer

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