NCX Senior Engineer

What you'll do

• Build and deploy custom AI solutions including distributed training, inference optimization, and MLOps pipelines on NVIDIA platforms.
• Act as the primary technical contact for strategic partners to troubleshoot complex production problems and provide remote or on-site support.
• Deploy and manage AI workloads across DGX Cloud and CSP environments using Kubernetes, containers, and GPU scheduling systems.
• Profile and tune large-scale training and inference workloads to reduce latency, cost, and operational risk.
• Implement observability and SLO/SLA monitoring for high-performance AI infrastructure.
• Integrate NVIDIA reference architectures with partner control planes and customer data pipelines.
• Create implementation guides, runbooks, and post-mortem documentation to codify standard methodologies for scaling AI workloads.

What we're looking for

• BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
• 8+ years of experience in customer-facing technical roles such as Solutions Engineering, DevOps, Site Reliability, or ML Infrastructure Engineering.
• Expertise in Linux systems, distributed computing, Kubernetes, containers, and GPU scheduling on multi-tenant platforms.
• Experience supporting large-scale training and inference workloads for LLMs, generative models, or recommendation systems in production environments.
• Proficiency in Python and Go programming with hands-on experience using PyTorch or TensorFlow frameworks.
• Ability to lead technical investigations, resolve complex issues, and communicate architectures clearly to both engineering and leadership audiences.
• Experience with the NVIDIA ecosystem including DGX systems, CUDA, NeMo, Triton, NIM, and networking technologies like InfiniBand and RoCE.
• Familiarity with MLOps, CI/CD pipelines, observability stacks, and infrastructure as code tools like Terraform or Ansible.