Key Roles & Responsibilities
Deploy and manage ML/LLM models in production settings.
Design and execute model load testing and performance benchmarking (latency, throughput, memory,
cost).
Build and optimize multi-node, multi-GPU training pipelines.
Configure and tune distributed training frameworks (data parallelism, model parallelism, pipeline parallelism).
Optimize GPU utilization, memory footprint, and inference costs.
Set up CI/CD pipelines for model deployment and retraining.
Troubleshoot GPU, networking, and performance bottlenecks.
Work across cloud platforms to ensure portability and vendor-agnostic deployments.
Required Skill Sets
Robust experience with GPU workloads (NVIDIA GPUs, CUDA concepts).
Proven expertise in model deployment on AWS, Azure, and GCP.
Hands-on experience deploying models up to 20B parameters.
Experience with distributed training (multi-node, multi-GPU setups).
Deep understanding of load testing, stress testing, and benchmarking ML systems.