AI Infrastructure Engineer

AI Infrastructure Engineers build scalable, reliable infrastructure for training and serving AI models. They design distributed systems, optimize GPU clusters, and create platforms that enable ML teams to work efficiently at scale.

Key Responsibilities: • Design and maintain AI training infrastructure • Optimize GPU cluster utilization • Build model serving platforms • Implement distributed training systems • Monitor infrastructure performance and costs • Create internal tools for ML teams

Required Skills: Kubernetes, distributed systems, GPU optimization, CUDA, cloud platforms, infrastructure as code, Python, Docker, Ray/Horovod, monitoring tools, cost optimization, network architecture

Career Path: Junior infrastructure engineers maintain existing systems. Mid-level engineers design new infrastructure components. Senior engineers architect AI platforms. Principal engineers set infrastructure strategy.

Top Hiring Companies: Meta, Google, OpenAI, Microsoft, Amazon, NVIDIA, Tesla, Anthropic, Databricks, Anyscale, Run.ai, Determined AI, Grid.ai