Must have

• 3–6 years of experience in AI/ML infrastructure engineering or high-performance computing (HPC).
• Solid experience with GPU-based systems, container orchestration, and AI/ML frameworks.
• Familiarity with distributed systems, performance tuning, and large-scale deployments.
• Expertise in modern GPU architectures (e.g., NVIDIA A100/H100, AMD MI300), multi-GPU configurations (NVLink, PCIe, HBM), and accelerator scheduling for AI training and inference workloads.
• Good understanding of modern AI model architectures, including LLMs (e.g., GPT, LLaMA), diffusion models, and multimodal encoder-decoder frameworks, with awareness of their compute and scaling requirements.
• Knowledge of leading AI/ML frameworks (e.g., TensorFlow, PyTorch), NVIDIA’s AI stack (CUDA, cuDNN, TensorRT), and open-source tools like Hugging Face, ONNX, and MLPerf for model development and benchmarking.
• Familiarity with AI pipelines for supervised/unsupervised training, fine-tuning (PEFT/LoRA/QLoRA), and batch or real-time inference, with expertise in distributed training, checkpointing, gradient strategies, and mixed precision optimization 

• AI Infrastructure Design & Deployment with multi-GPU clusters using NVIDIA or AMD platforms.
• Configure GPU environments using CUDA, DGX Systems, and NVIDIA Kubernetes Device Plugin.
• Deploy and manage containerized environments with Docker, Kubernetes, and Slurm.
• AI Model Support & Optimization for training, fine-tuning, and inference pipelines for LLMs and deep learning models.
• Enable distributed training using DDP, FSDP, and ZeRO, with support for mixed precision.
• Tune infrastructure to optimize model performance, throughput, and GPU utilization.
• Design and operate high-bandwidth, low-latency networks using InfiniBand and RoCE v2.
• Integrate GPUDirect Storage and optimize data flow across Lustre, BeeGFS, and Ceph/S3.
• Support fast data ingestion, ETL pipelines, and large-scale data staging.
• Leverage NVIDIA’s AI stack including cuDNN, NCCL, TensorRT, and Triton Inference Server.
• Conduct performance benchmarking with MLPerf and custom test suites

• NVIDIA Certified Professional – Data Center AI
• Kubernetes Administrator (CKA)
• CCNP or CCIE Data Center 
• Cloud Certification (AWS, Azure, or GCP

Educational Qualifications

• Batchlors in Computer Science/Applications/BTech Computer Science/MCA

• GPU Infrastructure Design & Optimization (NVIDIA A100/H100, AMD MI300)
• CUDA Programming & NVIDIA DGX Systems Setup
• Containerization with Docker, Kubernetes, and NVIDIA Device Plugin
• Distributed AI Training (DDP, FSDP, ZeRO, Mixed Precision)
• PyTorch, TensorFlow, and Model Optimization using TensorRT
• High-Performance Networking (InfiniBand, RoCEv2, GPUDirect Storage)
• AI Model Deployment using Triton Inference Server
• Data Management for AI Pipelines (Lustre, BeeGFS, Ceph, S3)
• Infrastructure Performance Benchmarking (MLPerf, NCCL Tests)
• Experience with LLMs and AI Model Scaling Requirements 
   

• Slurm Workload Manager for Scheduling AI Jobs
• PEFT/LoRA/QLoRA-based Fine-tuning Strategies
• Open-Source AI Tools – Hugging Face, ONNX, FastAPI for Model Serving
• Integration with ETL/Data Ingestion Pipelines (Kafka, Spark, Airflow)
• GPU Memory Optimization – HBM Utilization, GPU Resource Scheduling
• AI Pipeline Automation using Python, Bash, or Terraform
• Basic Cloud Infrastructure Knowledge (AWS EC2 GPU Instances, Azure ML, GCP Vertex AI)
• Monitoring & Logging (Prometheus, Grafana, NVIDIA DCGM, ELK Stack)
• Hybrid Cloud Setup for AI Workloads
• CI/CD Pipelines for ML Ops (GitHub Actions, MLflow, Kubeflow Pipelines)