Remote GPU & ML Infrastructure Engineer

ConsultBae India Private limited; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

ConsultBae India Private Limited is seeking a seasoned GPU & ML Infrastructure Engineer to build, test, monitor, and maintain GPU-based benchmarking and ML workloads. You will own end-to-end data generation, telemetry collection, and reproducible results.

Experience with NVIDIA data-center GPUs, Linux systems, and automation is essential to deploy workloads, validate data, and document platform configurations across edge and data-center platforms.

Qualifications

• 10 years of relevant experience preferred; exceptional candidates considered.
• Hands-on experience deploying LLM workloads on GPUs.
• Experience with quantized and full-precision ML models.
• Strong Linux system administration and troubleshooting skills.
• Experience collecting and analyzing hardware telemetry programmatically.
• Telemetry technologies such as NVML, DCGM, BMC, IPMI, Redfish.

Responsibilities

• Port and adapt GPU test procedures to NVIDIA data-center and edge platforms.
• Analyze driver differences, power/thermal limits, and telemetry sources.
• Build and maintain benchmarking, stress-testing, and workload suites.
• Deploy and execute LLM inference/training workloads, including Llama-family models.
• Ensure reproducibility and consistency across runs.
• Automate deployment, logging, data collection, and cleanup.
• Build data-quality checks for time-series and hardware telemetry data.
• Document platform configurations, limitations, and hardware behavior.

Skills

LLM inference

LLM training

NVIDIA GPU environments

Linux administration

Telemetry programmatically

Automation scripting

Multi-GPU scaling

NCCL

Tensor parallelism

Pipeline parallelism

Data-quality validation

Tools

NVML

DCGM

BMC

IPMI

Redfish

Job description

ConsultBae India Private Limited is seeking a seasoned GPU & ML Infrastructure Engineer to build, test, monitor, and maintain GPU-based benchmarking and ML workloads. You will own end-to-end data generation, telemetry collection, and reproducible results.

Experience with NVIDIA data-center GPUs, Linux systems, and automation is essential to deploy workloads, validate data, and document platform configurations across edge and data-center platforms.