Roles & responsibilities:
Skill Sets:
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- Deploy and manage ML/LLM models (<20B parameters) in production environments
- 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
Skill Sets:
- Strong 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
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