Experience: 10+ Years preferred Exceptional Candidates: 8+ Years may be consideredLocation: Remote Working Hours: Must be able to provide overlap until 11:00 AM PSTAbout the RoleWe are looking for an experienced GPU & ML Infrastructure Engineer to build and maintain reliable infrastructure for GPU-based benchmarking, stress testing, telemetry collection, and machine learning workloads.The engineer will work across multiple NVIDIA data-center GPU generations and embedded/edge platforms, ensuring that workloads, hardware telemetry, data collection, and deployment processes are consistent, repeatable, and reliable across different hardware environments.The role will own the end-to-end data generation process, including porting test procedures to new hardware, automating deployments, executing controlled GPU workloads, validating telemetry, performing data-quality checks, and delivering well-documented datasets.Key ResponsibilitiesPort and adapt existing GPU test procedures to new data-center GPUs and edge/embedded platforms.Analyze differences in GPU drivers, power/thermal limits, hardware sensors, and available telemetry sources across platforms.Build and maintain controlled GPU benchmark and workload suites.Deploy and execute LLM inference and training workloads, including Llama-family models in both quantized and full-precision configurations.Develop and maintain synthetic workloads such as:GEMMConvolutionCompute workloadsMemory-bandwidth workloadsMulti-GPU workloadsVision models for edge platformsEnsure GPU workloads are reproducible and deterministic across repeated runs.Collect and validate hardware telemetry from:NVIDIA Management Library (NVML)Data Center GPU Manager (DCGM)BMCIPMIRedfishOther external/lab-grade measurement equipmentEnsure consistent sampling rates, timestamps, field naming, and clock alignment across telemetry sources.Diagnose missing, irregular, inconsistent,



or inaccurate sensor data and identify root causes.Automate infrastructure deployment, workload execution, logging, data collection, and cleanup.Build automated data-quality validation checks for time-series and hardware telemetry data.Detect missing samples, clock mismatches, telemetry/workload misalignment, and sensors returning invalid or no data.Ensure collected datasets are stored in a consistent and documented structure.Maintain complete run metadata, including hardware model, driver version, firmware version, procedure version, and execution schedule.Document configuration, limitations, and behavior of each supported hardware platform.Required Skills & Experience10+ years of overall relevant experience preferred; exceptional candidates with 8+ years may be considered.Hands-on experience deploying LLM inference and training workloads on GPUs.Experience working with quantized and full-precision ML models.Strong experience with NVIDIA GPU environments and GPU infrastructure.Solid Linux systems administration and troubleshooting skills.Good understanding of:GPU driver stacksLinux processesProcess orchestrationSchedulingTiming behaviorHands-on experience collecting and analyzing hardware telemetry programmatically.Experience with telemetry technologies such as:NVMLDCGMBMCIPMIRedfishAbility to independently diagnose sensor and sampling issues in hardware time-series data.Strong understanding of workload reproducibility and controlling sources of non-determinism.Experience with automation and scripting for infrastructure deployment and workload execution.Good to HaveExperience developing data-collection pipelines for:Hardware testingHardware qualificationSystems researchPerformance benchmarkingExperience with GPU benchmark and stress-testing tools.Understanding of benchmark methodology, including:Warm-upSteady-state executionRun-to-run variancePerformance consistencyUnderstanding of GPU power and thermal management.Familiarity with GPU clock-throttling reasons and related telemetry.Experience with multi-GPU scaling.Knowledge of NCCL.Experience with tensor parallelism and pipeline parallelism.Experience developing automated data-quality validation for time-series or sensor data.Key Technical AreasGPU: NVIDIA Data Center GPUs, Embedded/Edge GPUsML Workloads: LLM Inference, LLM Training, Llama-family Models, Vision ModelsGPU APIs/Telemetry: NVML, DCGMHardware Management: BMC, IPMI, RedfishSystems: Linux, GPU Drivers, Process OrchestrationBenchmarking: GEMM, Convolution, Compute, Memory Bandwidth, Stress TestingMulti-GPU: NCCL, Tensor Parallelism, Pipeline ParallelismData: Time-Series Telemetry, Data Validation, Dataset GenerationCandidate ProfileThe ideal candidate is an experienced GPU/ML infrastructure engineer who can work independently across hardware, Linux systems, machine learning workloads, telemetry, automation, and data-quality validation.The candidate should be comfortable taking ownership of the complete workflow—from preparing a new GPU platform and deploying workloads to collecting reliable telemetry and producing reproducible, well-documented datasets.Work ArrangementLocation: RemoteRequired Overlap: Until 11:00 AM PSTExperience: 10+ years preferred; 8+ years may be considered for exceptional candidates

Pay: ₹150,000.00 - ₹175,000.00 per month

Benefits:

- Provident Fund

Work Location: Remote

📌 GPU & ML Infrastructure Engineer(Exp : 10+yrs) (India)
🏢 ZecData Technology
📍 India