Senior MLOps Engineer - DSX Enablement
NVIDIA Corporation (company site, opens in a new tab)8+ yrsBengaluruOn-siteDirect career pageEst. 35–65 LPA
Posted on 2 Sept
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Job description
Structured overview of role & requirements
About This Role
- Develop and deploy custom AI solutions on NeoCloud platforms and NVIDIA Cloud Partners, focusing on distributed training, inference optimization, and MLOps pipelines.
- Serve as primary technical contact for internal and external customers to guide joint engagements and ensure initiative success on DGX Cloud.
- Profile and optimize large-scale training and inference workloads, develop open-source tools and reference architectures supporting scalable ML and AI workloads.
Minimum Requirements
- Bachelor’s, Master’s, or Ph.D. in Computer Science, Computer/Electrical Engineering or related technical field, or equivalent experience.
- 8+ years experience in technical roles such as data science, data engineering, or ML engineering targeting large-scale production systems.
- Experience with Linux, batch schedulers, Kubernetes, distributed filesystems, advanced datacenter-scale networking; scripting in bash/Python; systems programming in C++, Go, or Rust.
- Experience using machine learning or deep learning frameworks for training and inference.
Ideal Candidate Profile
- Demonstrated ability to handle full ML lifecycle including production scale systems and troubleshooting across hardware, networking, OS, and system layers.
- Experience working closely with teams building infrastructure software and accelerated AI frameworks, particularly in NVIDIA ecosystem components such as DGX, CUDA, Triton, or NeMo.
- Background in MLOps practices within cloud-native environments including containerization, CI/CD, workflow automation, and observability stacks.