Senior ML Ops Engineer
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Job Description
Senior Engineer deploying NVIDIA AI infrastructure, distributed workloads, and MLOps solutions for NVIDIA cloud partners. Supporting production AI deployments across DGX Cloud, NCP, and major CSP environments.
Responsibilities:
- Develop innovative solutions that advance AI infrastructure capabilities
- Build and deploy custom AI solutions on NCP and Neo Cloud platforms, including distributed training, inference optimization, and MLOps pipelines
- Act as the main technical contact for strategic NCPs
- Provide remote and on-site support
- Troubleshoot complex production problems and guide partner engineering teams on NVIDIA platform guidelines
- Deploy and manage AI workloads across DGX Cloud, NCP data centers, and major CSP environments
- Use Kubernetes, containers, and GPU scheduling systems aligned to NCP builds
- Profile and tune large-scale training and inference workloads
- Implement observability and SLO/SLA monitoring
- Reduce latency, cost, and operational risk
- Implement and expand NVIDIA reference architectures on partner platforms
- Develop integrations with partner control planes and customer environments
- Ensure API, data pipeline, and enterprise software connectivity
- Build implementation guides, runbooks, and post-mortem documentation
- Codify standard methodologies for running NVIDIA AI workloads at scale
Requirements:
- 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
- Experience supporting large-scale cloud or service provider environments
- Strong expertise in Linux systems, distributed computing, Kubernetes, containers, and GPU scheduling on multi-tenant or service-provider platforms
- Demonstrated AI/ML experience supporting large-scale training and inference workloads in production or critically important environments
- Solid programming skills in Python/Go
- Hands-on experience using PyTorch or TensorFlow for training and serving
- Ability to collaborate with customer and partner engineering teams, guide technical investigations, and resolve issues
- Excellent communication and technical presentation skills
- Experience with NVIDIA ecosystem, including DGX systems, CUDA, NeMo, Triton, NIM, InfiniBand, and RoCE
- Experience collaborating with NVIDIA Cloud Partners, hyperscale CSPs, or managed AI cloud platforms
- Familiarity with MLOps and cloud-native practices, containerization, CI/CD, observability stacks, and GitOps workflows
- Infrastructure-as-code experience with Terraform, Ansible, or similar tools
Benefits:
- Competitive salaries
- Generous benefits package
- Equity
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