AI Infrastructure Engineer - Python/LLM/GPU

Title : AI Infrastructure Engineer (Python | LLM | GPU | AWS)

Location : Bangalore / Hyderabad / Pune / Chennai / Mumbai / Noida

Experience : 5+ Years in AI/ML Infra role, Python, Pytorch, Amazon Sagemaker, Multi-GPU

Overview :

We are looking for a Senior AI Infrastructure Engineer with hands-on experience building and optimizing AI infrastructure for Large Language Models (LLMs). The ideal candidate should have strong expertise in Python, PyTorch, AWS, GPU-based AI workloads, and production-scale ML systems.

Required Skills :

• 5+ years of experience in AI Infrastructure, ML Platform Engineering, MLOps, or Machine Learning Infrastructure.

• Strong programming experience in Python.

• Hands-on experience with PyTorch.

• Experience deploying, fine-tuning, or servingLarge Language Models (LLMs).

• Experience building AI pipelines using Amazon SageMaker.

• Hands-on experience working with GPU-based AI workloads.

• Strong understanding of GPU architecture and performance optimization.

• Experience with distributed model training and multi-GPU environments.

• Good understanding of AWS services including : EC2 GPU Instances, SageMaker, S3, VPC, Networking.

• Experience designing scalable production Machine Learning systems.

• Good understanding of MLOps, model deployment, monitoring, and production lifecycle.

Key Responsibilities :

• Design, deploy, and manage Large Language Models (LLMs) on AWS infrastructure.

• Build, optimize, and maintain model training, fine-tuning, and inference pipelines using Amazon SageMaker.

• Optimize GPU-based AI workloads for training and inference performance.

• Design scalable ML infrastructure supporting distributed training and inference.

• Work with NVIDIA GPU environments (A100, H100) and AWS AI services including Trainium and Inferentia.

• Improve compute, networking, and storage performance for large-scale AI workloads.

• Benchmark model performance and optimize infrastructure for latency, throughput, and cost.

• Troubleshoot infrastructure bottlenecks across GPUs, distributed systems, and cloud environments.

• Collaborate with AI researchers, ML engineers, and platform teams to build production-ready AI systems.

• Implement infrastructure best practices for scalability, monitoring, reliability, and security.

Preferred Skills (Not Mandatory) :

• Experience working with NVIDIA A100/H100 GPUs.

• Exposure to AWS Trainium and Inferentia.

• Experience with Hugging Face Transformers and LLM deployment.

• Experience with distributed training frameworks.

• Experience with Kubernetes and Docker.

• Understanding of high-performance computing (HPC) or distributed systems.

• Exposure to model optimization techniques such as quantization, pruning, or knowledge distillation.

• Experience building high-throughput, low-latency AI inference systems.