NVIDIA GPU Infrastructure Engineer / HPC Engineer

• *Why Choose Balbhas?**
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Join a team that's transforming how businesses understand and leverage their data. We're not just building analytics solutions — we're shaping the future of digital intelligence.

### **Competitive Salary**

Industry\-leading compensation packages with performance bonuses and equity options.

### **Flexible Work**

Remote\-first culture with flexible hours and modern collaborative workspaces.

### **Learning \& Development**

Continuous learning opportunities with training budgets and conference attendance.

### **Health \& Wellness**

Comprehensive health insurance, wellness programs, and mental health support.

### **Team Culture**

Collaborative environment with team building events and inclusive culture initiatives.

### **Work\-Life Balance**

Generous PTO policy, sabbatical opportunities, and family\-friendly benefits.

### **NVIDIA GPU Infrastructure Engineer / HPC Engineer**

Full\-time

Chennai and DC Sriperambatur

4\.0\+ years

Position NVIDIA GPU Infrastructure Engineer / HPC Engineer Job Purpose Build and operate large‑scale GPU compute pods to deliver predictable, high‑throughput, low‑latency training and inference services across 10K GPU cluster. Role Description Key Responsibilities Implementation Stand up multi‑pod GPU clusters (rack/power/cooling layouts; TOR/leaf connectivity; IB/Ethernet host configs). Implement GPU partitioning (MIG/vGPU profiles) and quota policies for multi‑tenant environments. Integrate cluster schedulers (Slurm/Kubernetes) with GPU device plugins, node feature discovery, and accounting/quotas. Operations Own day‑2 operations across firmware/driver/DCGM/NVML lifecycles; execute change windows with zero/low downtime. Capacity planning (GPU/CPU/Memory/NIC) and bin‑packing strategies for heterogeneous GPU SKUs. Performance \& Optimization Tune NCCL/UCX, GPU clocks/persistence, GPU Direct Storage, NUMA/locality, and CUDA runtime parameters. Drive benchmarking and acceptance (HPL, HPL‑AI, MLPerf‑like internal suites); track perf regressions with SLOs. Reliability \& Incident Lead P0/P1 incident response for GPU, CUDA, or scheduler issues; perform root‑cause and preventative actions. Act as the primary technical lead during production outages, coordinating cross\-functional teams across Networking, GPU Operations, Platform Engineering, Storage, and Application teams to restore services within SLA targets. Perform detailed Root Cause Analysis (RCA) for network, GPU, CUDA, NCCL, RDMA, and scheduler\-related failures, identifying underlying causes and implementing preventive and corrective actions. Collaborate with platform and GPU engineering teams to resolve issues impacting CUDA jobs, Kubernetes scheduling, Slurm workload management, GPU resource allocation, and large\-scale AI training environments. Define golden images; implement node remediation (cordon/drain/reimage) and auto‑healing workflows. Security \& Compliance Enforce GPU tenancy isolation (MIG, cgroup/device cgroup, mpsd), secure drivers/containers, SBOM and image scanning. Documentation \& Enablement Publish runbooks, performance baselines, and application tuning guides for LLM training and inference. Experience \& Educational Requirements Qualifications and Experience EDUCATIONAL QUALIFICATIONS: (degree, training, or certification required) BE/B\-Tech or equivalent with Computer Science or Electronics \& Communication RELEVANT EXPERIENCE: (no. of years of technical, functional, and/or leadership experience or specific exposure required) 7–12 years building/operating HPC/AI GPU clusters at scale; deep CUDA/MIG/vGPU expertise. Proven with H100/B‑series class GPUs, Slurm and/or Kubernetes device scheduling; NCCL/UCX performance tuning. Tools / Tech 1\. CUDA, NCCL, cuDNN, TensorRT; 2\. DCGM/NVML, nvidia\-smi; 3\. Slurm, K8s (device plugin, DaemonSets, NFD); 4\. Helm/ArgoCD; 6\. GPUDirect Storage; 7\. Prometheus/Grafana; 8\. ELK/Splunk. Certifications NVIDIA Certified Professional (AI Infrastructure); Linux (RHCE/LPIC) preferred. KPIs GPU utilization %, queue wait‑time SLOs, failed‑job rate, perf baseline adherence, MTTD/MTTR for GPU incidents. Work Mode On‑site/Hybrid; participates in change windows and critical after‑hours events.

• *Our Culture \& Values**
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### **Innovation First**

We encourage creative thinking and bold ideas that push the boundaries of what's possible in analytics.

### **Collaborative Excellence**

Our diverse teams work together to solve complex challenges and deliver exceptional results for our clients.

### **Continuous Growth**

We invest in our people's development and provide opportunities to learn, grow, and advance their careers.