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

• Solid 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.

📌 Devops Mlops+aiops Engineer Gurugram (India)
🏢 Profex Tech
📍 India