Job Title

Must have deep Linux, GPU and Python knowledge.We are seeking an experienced Linux & GPU Engineer with deep expertise in NVIDIA GPU technologies to support and scale our cloud and on-premise software platform. You will play a key role in building and optimizing compute environments for intensive scientific, engineering, and AI workloads—ranging from large-scale simulations to deep learning training clusters.This position is ideal for someone who thrives at the intersection of GPU computing, Linux, parallel computing, and high-throughput systems, and who’s passionate about enabling researchers, engineers, and scientists to work at scale.Work from Home! Compensation 11 LPA. Please do not apply if compensation is not acceptable.You'll work with our founders and US members plus off-shore cloud and AI team.Responsibilities:Design, implement, and manage GPU Cloud and on-premise software with a focus on NVIDIAGPU Install, configure, and maintainGPU drivers ,CUDA libraries , andNVIDIA software stacks(e.g., NCCL, cuDNN etc). Create virtual machines for AI tools and packages Use Linux and networking knowledge to simplify and make virtual machine deployment smooth Help customers in domains like AI, simulation, machine learning, data analytics, and scientific computing.with installation and operations Work on advanced clustering, scheduling and workload management systems such asSlurm Work with containerized environments withKubernetesandDocker . Collaborate with researchers and engineers to profile, optimize, and troubleshoot GPU-intensive workloads. Monitor system health, job performance, and GPU utilization across nodes. Contribute to architecture decisions, scaling strategies, and automation of infrastructure provisioning and maintenance.Required Skills and Experience :3 years of experience in Linux and GPU hardware and tools such as MIG for GPU partitioning etc Hands-on experience withNVIDIA GPUsin a production or research computing setting. Understanding of HPCworkload managers(e.g., Slurm) and job scheduling policies. Familiarity withparallel computing(MPI, OpenMP) and large-scale system architecture. Experience withLinux system administrationand scripting languages (e.g., Bash, Python). Familiarity withhigh-speed interconnects(e.g., InfiniBand, RDMA). Strong problem-solving and communication skills.Preferred Qualifications :Experience withAI/ML workflows(e.g., TensorFlow, PyTorch on GPUs). Exposure tocontainer orchestration, using tools like Singularity or Kubernetes. Experience withGPU monitoringand observability tools (e.g., DCGM, Prometheus, Ganglia). Familiarity withautomation tools(e.g., Ansible, Terraform). Proficiency with CUDA programming, GPU performance tuning, and hardware benchmarking. NVIDIA certifications (e.g., DLI, CUDA Developer).