Lead AI Engineer

In this role, you will:

• Architect and implement distributed AI runtime systems with elastic scaling and job recovery.
• Optimize performance at low levels (CUDA, NCCL, PyTorch internals) for multi-GPU workloads.
• Develop custom runtime architectures for large-scale AI training pipelines.
• Integrate orchestration tools like Kubernetes, Ray, TorchElastic, Horovod for containerized AI workloads.
• Implement fault recovery mechanisms and observability hooks for runtime health monitoring.
• Collaborate with AI researchers and platform engineers to ensure efficient resource utilization and throughput optimization.
  • Contribute to CI/CD pipelines for AI infrastructure and runtime deployments.