AI Infrastructure Engineer

We develop and scale Agentic AI systems and Industry Foundation Models that transform engineering, manufacturing, and enterprise processes at the BMW Group. As an internal AI consulting and engineering organisation, we work directly with business domains to identify high-impact use cases, build production-ready AI solutions, and establish secure, scalable platforms to train, deploy, and operate them.

• You architect cloud and hardware solutions to scale AI workloads across GPUs and accelerators, while optimising storage and networking for throughput, resilience, and cost efficiency.
• From fine-tuning and inference to large-scale model serving, you engineer, deploy, and operate production-grade AI systems and Industry Foundation Models.
• You build and continuously enhance platform capabilities for reliable deployments, Infrastructure as Code, observability, monitoring, and end-to-end model lifecycle management.
• By designing scalable data pipelines and AI data flywheels, you ensure access to high-quality data and enable continuous learning.
• Working closely with enterprise platforms, you develop robust AI services and integrate them securely while maintaining reliability and operational excellence.
• A key part of your role is designing secure-by-default AI infrastructure, including identity and access management, infrastructure hardening, secrets management, and enterprise-grade security controls.

• University degree in Computer Science, Systems Engineering, or a comparable technical discipline.
• At least four years of professional experience designing, building, and operating modern cloud, AI, or platform infrastructure in production environments.
• Hands-on experience with AWS, Azure, and high-performance GPU clusters, including NVIDIA GPUs, CUDA, NCCL, and InfiniBand.
• Strong programming skills in Python with experience in Infrastructure as Code, APIs, CI/CD, automation, and observability.
• Practical experience with PyTorch and distributed AI training technologies such as Ray, Slurm, or Kubernetes.
• Strong understanding of cloud security, enterprise infrastructure security, identity and access management, networking, and platform hardening.
• Commitment to continuous learning, including current developments in AI infrastructure, cloud platforms, platform engineering, cybersecurity, and emerging AI technologies.