Ofertas › Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen
Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen · Göttingen, Germany · 55.000 - 75.000 € per year
Salary: 55.000 - 75.000 € per year Requirements: Masters degree (or equivalent) in Computer Science, Electrical Engineering, Data Science, or a related field Experience in HPC systems, storage infrastructure, and AI data engineering Proven experience with high-performance storage technologies (e.g., Lustre, GPFS, Ceph, NFS, S3-compatible storage) Strong knowledge of AI/ML data pipelines, including data ingestion, preprocessing, and model training workflows Hands-on experience with containerization (Docker, Kubernetes) and orchestration tools in HPC/AI environments Familiarity with cloud storage platforms Experience in data lifecycle management, data versioning, and metadata management Solid understanding of data security, privacy (GDPR), and compliance in AI applications Excellent analytical and problem-solving skills Excellent communication skills and ability to translate technical concepts for non-technical stakeholders Very good German skills (C1) Very good English skills (C1) Willingness to travel on business Desirable: Experience in research or innovation projects related to HPC, AI, or data infrastructure Desirable: Knowledge of AI-driven storage optimization (e.g., predictive data placement, auto-tiering) Desirable: Familiarity with open-source HPC/AI frameworks (e.g., Slurm, Singularity, MLflow, Kubeflow) Desirable: Proficiency in scripting languages (Python, Bash) and system administration (Linux/UNIX) Responsibilities: Design, implement, and optimize high-performance storage solutions tailored for AI and HPC workloads Conduct research on scalable, cost-effective storage architectures suitable for AI training and HPC simulations in resource-constrained environments Develop and deploy data management strategies for large-scale AI datasets, including data lifecycle management, versioning, backup, and security (e.g., GDPR-compliant data handling) Investigate and prototype AI-optimized storage systems leveraging technologies such as NVMe, SSD caching, and distributed storage (e.g., Ceph, MinIO, Lustre) Support SMEs in evaluating and selecting appropriate HPC and AI storage solutions Collaborate with data scientists and engineers to ensure storage systems meet the demands of AI model training, inference, and real-time processing Develop best practice guidelines and toolkits for SMEs to manage AI/HPC data efficiently and securely Provide technical advisory and training to SME teams on data storage, performance monitoring, and data governance Monitor an