AI Infrastructure Engineer

Design, implement and operate highly available, scalable and secure infrastructure platforms for enterprise AI/ML and GenAI workloads.

Key Responsibilities

Design enterprise AI infrastructure architecture.

Design GPU compute platforms for AI workloads.

Architect Kubernetes-based AI platforms.

Design cloud and hybrid AI infrastructure.

Implement GPU orchestration and resource management.

Design model-serving infrastructure.

Implement MLOps/LLMOps infrastructure.

Implement Infrastructure-as-Code.

Design CI/CD pipelines for AI workloads.

Implement monitoring, logging and observability.

Optimize GPU utilization and AI infrastructure cost.

Design high-availability and disaster-recovery architecture.

Implement infrastructure security and hardening.

Integrate AI infrastructure with enterprise IAM.

Design secure networking for AI platforms.





Support large-scale LLM deployment.

Work with AI architects, cybersecurity teams and DevOps teams.

Lead infrastructure troubleshooting and performance optimization.

Mentor junior AI infrastructure engineers.

Core Technical Skills

Linux

Kubernetes

Docker

NVIDIA GPU/CUDA

AWS / Azure / GCP

Terraform

Ansible

CI/CD

Python

Bash

MLOps / LLMOps

GPU orchestration

Model serving

Observability

Networking

Storage

IAM

High Availability / DR

Preferred Certifications

AWS Solutions Architect

Azure Solutions Architect

Google Cloud Architect

CKA/CKAD

Terraform

NVIDIA certifications

Relevant MLOps/AI infrastructure certifications

📌 AI Infrastructure Engineer -Delhi
🏢 Aelius Consultants Delhi
📍 Delhi