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Tier-1 employer, Bengaluru metro location, and broad skillset requirements increase qualified applicant density.
Deep ML systems, accelerator and DGX expertise make background highly domain-specific.
Explicit 8+ years, mandatory ML lifecycle and systems stack requirements make filters strict.
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.
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.
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.