AI Engineer

• Design and scale services to orchestrate AI/ML clusters across cloud and on-prem environments
• Develop and optimize intelligent scheduling and resource management systems for heterogeneous compute clusters
• Integrate Ray Train/Tune for large-scale distributed training workflows and Ray Serve for low-latency, autoscaled inference
• Build features to improve reliability, performance, observability, and cost-efficiency of AI workloads at scale
• Enhance the control plane to support secure multi-tenancy and enterprise-grade governance
• Implement systems for container management, dependency resolution, and large-scale model distribution
• Collaborate with ML researchers, applied scientists, and distributed systems engineers to drive platform innovation
• Provide production support and work closely with field teams to resolve infrastructure issues

AI Platform Systems Software Engineer

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Fullstack AI Engineer