DevOps (MLOps+AIOps) Engineer

Key Roles & Responsibilities • Deploy and manage ML/LLM models 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. Required 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.