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    Job · 20983333

    CHENNAI · FULLTIME

    GPU Engineer & DevOps Engineer AI/Cloud Infrastructure (Chennai, Onsite)

    Cinergy Technology Inc

    Cinergy Technology Inc

    Chennai · onsite · Posted 9d ago

    Sourced fromFoundit
    Not disclosed6–8 yrsfulltimeChennai

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    Section · 01

    About this role

    We're hiring GPU Engineers and DevOps Engineers in Chennai for a 1-year onsite contract supporting AI and Cloud infrastructure projects for a confidential enterprise AI infrastructure client.

    Open Positions: 20 positions across GPU Engineer and DevOps Engineer roles

    Experience: 6+ years of relevant, hands-on production experience

    Location: Chennai — onsite only. Candidates must be currently local to Chennai and willing to work onsite full-time.

    Contract: 1 year, extendable based on performance and project needs. Rate/salary discussed based on experience and technical expertise.

    GPU Engineer — Key Responsibilities

    • Deploy, configure, and maintain GPU-based AI/ML infrastructure
    • Work hands-on with NVIDIA GPUs, CUDA, GPU drivers, and GPU compute environments
    • Troubleshoot GPU performance, utilization, memory, and compute issues
    • Support AI/ML workloads across cloud and on-premise environments
    • Work with Docker and Kubernetes for containerized GPU workloads
    • Optimize GPU resource utilization and workload performance
    • Monitor GPU infrastructure health, capacity, and performance
    • Collaborate closely with AI/ML, DevOps, and infrastructure teams

    Required Skills:

    • 6+ years of relevant experience
    • Strong hands-on experience with NVIDIA GPUs and CUDA
    • Linux administration and troubleshooting
    • Docker and Kubernetes
    • GPU infrastructure deployment and troubleshooting
    • Experience with AWS, Azure, or GCP
    • Python and/or Bash scripting
    • GPU monitoring and performance optimization
    • Experience supporting production AI/ML infrastructure

    Good to Have:

    • NVIDIA GPU cluster experience
    • Kubernetes GPU scheduling
    • NVIDIA Container Toolkit
    • Slurm or other GPU workload schedulers
    • MLOps / AI infrastructure experience
    • Large-scale GPU cluster experience

    DevOps Engineer — Key Responsibilities

    • Design, deploy, and maintain cloud infrastructure
    • Build and manage CI/CD pipelines
    • Automate infrastructure provisioning and configuration
    • Manage Docker containers and Kubernetes environments
    • Implement Infrastructure as Code using Terraform
    • Monitor infrastructure, applications, and production environments
    • Troubleshoot deployment and production issues
    • Implement security, scalability, reliability, and automation best practices
    • Collaborate with development, AI/ML, and infrastructure teams

    Required Skills:

    • 6+ years of relevant DevOps experience
    • Strong experience with AWS / Azure / GCP
    • Docker and Kubernetes
    • Terraform / Infrastructure as Code
    • CI/CD — Jenkins, GitHub Actions, GitLab CI, Azure DevOps, or equivalent
    • Linux administration
    • Git and Python/Bash scripting
    • Monitoring and logging — Prometheus, Grafana, ELK, or equivalent
    • Strong production troubleshooting experience

    Good to Have:

    • AI/ML infrastructure experience
    • GPU infrastructure exposure
    • NVIDIA CUDA/GPU environments
    • Kubernetes cluster administration
    • Cloud security
    • MLOps experience

    Candidate Requirements (both roles)

    • 6+ years of relevant experience
    • Currently based in Chennai — local candidates only, no relocation
    • Willing to work onsite in Chennai full-time
    • Strong hands-on technical and production experience
    • Available for a 1-year contract engagement

    To apply: Please apply via LinkedIn Easy Apply and include your Total Experience, Relevant Experience (GPU or DevOps), Current Location, Notice Period, and Expected Rate/Salary in your application.

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    Section · 02

    Skills

    DockerTerraformBashKubernetesGPU infrastructure deployment and troubleshootingGPU monitoring and performance optimizationCudagoogle cloud platformamazon web servicesNVIDIA GPUsProduction troubleshootingMicrosoft AzureGITLinux

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