Associate Principal - Cloud Engineering

Ltm • Media & Entertainment • Creative Agencies • 2 hours ago
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Salary
$164k per year
Location
Remote (United States)
Seniority
Principal · 8+ years exp

Confirmed on the employer's own hiring board on Oct 16, 2026. First seen by Alion on Aug 18, 2026. Ltm scores B on the Alion truth index.

Overview
Company
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Profile match

Ltm

LTIMindtree is a worldwide technology consulting and digital solutions company that empowers businesses in a variety of sectors to get to the future faster by combining engineering DNA and tech expertise.
ltm.com • Bengaluru • Mumbai • Hyderabad • Chennai • Pune • Branding • Creative Agencies • Media & Entertainment • Enterprise Apps Implementation Partners • IT Outsourcing & Dedicated Teams • IT Consulting & Digital Transformation • Est. 1996
Tech stack of Ltm

100% Remote

Max Salary $164K+Benefits

Role Summary

The NVIDIA GPU Platform Engineer builds and operates the GPU and inferenceserving stack NVIDIA AI Enterprise on EKS NIM microservices and NVIDIA Dynamo serving and owns GPU sharing scheduling integration and serving performance Onshore placement supports handson GPUedge coordination and close work with the architecture team

Key Responsibilities

  • Deploy and operate NVIDIA AI Enterprise NVAIE on EKS GPU Operator drivers CUDA runtime and DCGM
  • Deploy and operate NIM microservices and NVIDIA Dynamo serving exposing the OpenAIcompatible API chatcompletions embeddings streaming batch
  • Configure GPU sharing MIG where the SKU supports it and RunAI fractional GPU as the isolation baseline and clearly distinguish hardware vs software isolation
  • Integrate RunAI for GPU scheduling quota and preemption and KEDA for replica autoscaling including scaletozero
  • Instrument GPU telemetry via DCGM and drive inference performance validation and SLAtier binding
  • Support the modelonboarding NIM packaging and inferencedeploy pipelines
  • Tune GPUserving performance and troubleshoot CUDA driver and scheduling issues
  • MustHave Skills Experience
  • 8 years in infrastructureML engineering with handson NVIDIA GPU operations
  • NVIDIA GPU stack drivers CUDA GPU Operator and DCGM
  • Model serving Triton NVIDIA Dynamo NIM TensorRTLLM andor vLLM
  • Kubernetes GPU workloads and device plugins
  • GPU scheduling RunAI or equivalent and GPU partitioning MIG fractional GPU
  • Autoscaling KEDA and OpenAIcompatible inference API patterns
  • NicetoHave Skills
  • NVAIE on AWSEKS and edge GPU Outposts G7
  • LLM RAG serving and vector databases
  • Inference performance benchmarking and optimization
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