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Senior MLOps Engineer – DSX Enablement

NVIDIA AI · Pachalik de Es-Semara

Senior 🇬🇧 English
Linux Batch schedulers Kubernetes Distributed filesystems Advanced networking Bash Python C++ Go Rust DGX systems CUDA RAPIDS NeMo

Description du poste

About the role

NVIDIA is looking for a Senior MLOps Engineer to join the DSX Enablement team. You will work directly with strategic customers and internal partners to design, build, and optimize AI and ML workloads on NVIDIA’s cloud platforms.

Key responsibilities

  • Develop and deploy custom AI solutions on NeoCloud and NVIDIA Cloud Partners, covering distributed training, inference optimization, and MLOps pipelines.
  • Serve as the primary technical contact for internal and external customers, guiding joint engagements and ensuring successful AI initiatives on DGX Cloud.
  • Collaborate with infrastructure and accelerated‑framework teams to address production‑grade AI and ML system challenges.
  • Profile, tune, and optimize large‑scale training and inference workloads to reduce latency, cost, and operational risk.
  • Create open‑source tools and reference architectures that simplify building and managing ML workloads at scale.

Required profile

  • BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or related field, or equivalent experience.
  • 8+ years in technical roles such as data science, data engineering, or ML engineering, preferably on large‑scale production systems.
  • Proven AI/ML experience across the full machine‑learning lifecycle, from exploratory analysis to production deployment.
  • Strong communication and presentation skills for both engineering and leadership audiences.

Required skills

  • Linux system administration
  • Batch schedulers (e.g., Slurm)
  • Kubernetes orchestration
  • Distributed filesystems and datacenter‑scale networking
  • Scripting in Bash and programming in Python
  • Systems programming in C++, Go, or Rust
  • Experience with machine‑learning frameworks for training and inference (e.g., TensorFlow, PyTorch)
  • Familiarity with NVIDIA ecosystem components such as DGX systems, CUDA, RAPIDS, NeMo

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NVIDIA AI

Pachalik de Es-Semara