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.
What should you bring along?
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.
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