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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive expertise in AI infrastructure development, including building and deploying custom AI solutions on cloud platforms. Proficient in managing large-scale training and inference workloads while ensuring optimal performance and reliability.
Highest-signal resume keywords
AI Infrastructure DevelopmentKubernetes and Container ManagementLarge-Scale Training and Inference WorkloadsPython/Go ProgrammingNVIDIA Ecosystem Experience
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI/ML ExperienceDistributed ComputingLinux SystemsMLOpsInfrastructure-as-CodeObservability and MonitoringAPI ConnectivityData Pipeline ManagementGPU SchedulingCloud-Native Practices
Soft Skills
Excellent CommunicationTechnical Presentation SkillsCollaboration with Engineering Teams
Tools & Technologies
NVIDIA DGX SystemsCUDAPyTorchTensorFlowTerraformAnsibleNIMNeMoTritonInfiniBand
Industry Keywords
Cloud PlatformsCSP EnvironmentsDevOpsSolutions EngineeringSite Reliability Engineering
Tech Stack
Tools & technologiesAnsibleCloudKubernetesLinuxPythonPyTorchTensorflowTerraformGo
About the role
Key responsibilities & impact- Develop innovative solutions that advance AI infrastructure capabilities
- Build and deploy custom AI solutions on NCP and Neo Cloud platforms
- Implement distributed training, inference optimization, and MLOps pipelines using NVIDIA reference architectures
- Act as the main technical contact for strategic NVIDIA Cloud Partners
- Provide remote and on-site support
- Troubleshoot complex production problems and guide partner engineering teams
- 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
Requirements
What you’ll need- 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
Benefits
Comp & perks- Equity
- Benefits
- Competitive salary
- Generous benefits package