Job Summary

We are seeking a highly skilled AI Infrastructure Engineer to design, implement, and maintain scalable infrastructure that supports artificial intelligence and machine learning workloads. The ideal candidate will have experience with cloud platforms, high-performance computing (HPC), GPU environments, containerization, and infrastructure automation. You will play a critical role in ensuring the reliability, performance, and scalability of AI systems used across the organization.

Key Responsibilities

• Design, deploy, and manage AI/ML infrastructure environments for training and inference workloads.
• Build and maintain high-performance computing (HPC) clusters optimized for AI applications.
• Configure and manage GPU-based systems and distributed computing environments.
• Optimize storage, networking, and compute resources to maximize performance and cost efficiency.
• Implement infrastructure automation using Infrastructure as Code (IaC) tools.
• Monitor system performance, availability, and security across AI platforms.
• Collaborate with Data Scientists, ML Engineers, and Software Developers to support AI model deployment and operations.
• Troubleshoot infrastructure bottlenecks and performance issues.
• Ensure platform scalability, reliability, disaster recovery, and business continuity.
• Manage containerized workloads using Kubernetes and Docker.
• Maintain cloud-based AI environments on AWS, Azure, or Google Cloud Platform.
• Establish security best practices for AI infrastructure and sensitive data.

Required Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.
• 3+ years of experience in cloud infrastructure, DevOps, Site Reliability Engineering (SRE), or AI platform engineering.
• Strong knowledge of Linux system administration.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Hands-on experience with Kubernetes, Docker, and container orchestration.
• Understanding of GPU computing technologies such as NVIDIA CUDA and GPU clusters.
• Experience with infrastructure automation tools like Terraform, Ansible, or CloudFormation.
• Knowledge of networking, storage systems, and distributed computing concepts.
• Familiarity with AI/ML frameworks such as TensorFlow, PyTorch, or JAX.

Preferred Qualifications

• Experience managing large-scale AI training environments.
• Knowledge of MLOps practices and tools.
• Experience with monitoring tools such as Prometheus, Grafana, ELK Stack, or Datadog.
• Familiarity with Apache Spark, Ray, or distributed AI frameworks.
• Relevant cloud certifications (AWS, Azure, or GCP).

Technical Skills

• Linux Administration
• Kubernetes & Docker
• Terraform / Ansible
• AWS / Azure / GCP
• GPU Infrastructure Management
• Python, Bash, or Go
• Networking & Storage Systems
• Monitoring & Observability
• CI/CD Pipelines
• AI/ML Platform Engineering

Salary Range

• Typical Salary: $170,000 – $230,000+ per year (depending on experience, location, and expertise)

Benefits

• Competitive compensation package
• Performance bonuses
• Health, dental, and vision insurance
• Remote or hybrid work options
• Professional development and certification support
• Access to cutting-edge AI technologies and projects