AI Infrastructure and Platform Architect

Position Summary

We are seeking an experienced AI Infrastructure and Platform Architect to design, optimize, and manage scalable AI infrastructure and platforms across on-premises, cloud, and hybrid environments.

The ideal candidate should have strong experience with GPU-based systems, AI/ML platforms, infrastructure architecture, performance optimization, capacity planning, and production support. The role will work closely with AI/ML developers, DevOps engineers, data engineers, and solution architects to improve the performance, reliability, scalability, and cost efficiency of AI solutions.

Key Responsibilities

• Design and manage AI infrastructure for model training, fine-tuning, inference, computer vision, Generative AI, and LLM workloads.
  
• Define CPU, GPU, RAM, VRAM, storage, networking, cooling, and power requirements.
  
• Review existing hardware and platform performance and recommend upgrades or optimizations.
  
• Perform capacity planning to support future workloads and minimize frequent hardware changes.
  
• Build and maintain AI platforms using Linux, Docker, Kubernetes, GPU orchestration, and cloud services.
  
• Configure and manage NVIDIA drivers, CUDA, cuDNN, TensorRT, and related AI acceleration technologies.
  
• Monitor system health, GPU utilization, memory usage, storage performance, and network throughput.
  
• Diagnose infrastructure failures, system crashes, performance bottlenecks, and platform outages.
  
• Implement monitoring, alerting, backup, disaster recovery, security, and operational best practices.
  
• Prepare architecture documents, hardware specifications, technical recommendations, and operational runbooks.
  
• Support production deployment, troubleshooting, and continuous platform improvement.
  

AI Solution Optimization

The candidate should also be capable of:

• Reviewing the end-to-end AI solution and identifying performance, architecture, and infrastructure gaps.
  
• Recommending improvements to scalability, reliability, maintainability, and cost efficiency.
  
• Supporting AI/ML developers with model training and experimentation environments.
  
• Helping reduce training time through GPU optimization, distributed training, resource tuning, and efficient data pipelines.
  
• Providing guidance on model accuracy, evaluation, hyperparameter tuning, and experimentation practices.
  
• Improving model-serving and inference performance.
  
• Mentoring existing team members on AI infrastructure and production-readiness best practices.
  

Required Skills

• AI infrastructure and GPU-based computing
  
• NVIDIA GPU architecture, CUDA, cuDNN, NCCL, and TensorRT
  
• Linux administration
  
• Docker and Kubernetes
  
• PyTorch, TensorFlow, Hugging Face, or similar frameworks
  
• Cloud and on-premises AI platforms
  
• Infrastructure sizing and capacity planning
  
• Performance monitoring and troubleshooting
  
• High-performance storage and networking
  
• MLOps, CI/CD, automation, and Infrastructure as Code
  
• Monitoring tools such as Prometheus, Grafana, NVIDIA DCGM, or OpenTelemetry
  

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Information Technology, Engineering, or a related field.
  
• Strong overall experience in infrastructure, cloud, platform engineering, architecture, or AI systems.
  
• A minimum of 3 years of direct, hands-on experience specifically working with AI infrastructure, machine learning platforms, GPU environments, or production AI workloads.
  
• Proven experience designing, deploying, supporting, or optimizing production AI systems.
  
• Strong problem-solving, troubleshooting, communication, and technical documentation skills.
  

The candidate's total professional experience may be significantly higher. However, at least three years should involve genuine, relevant, hands-on work with AI systems and platforms.

Added Advantage

Preference will be given to candidates who have experience with:

• AI solution architecture
  
• Large Language Models and Generative AI
  
• RAG and agentic AI systems
  
• Distributed model training
  
• Computer vision and edge AI
  
• Model-serving platforms
  
• AI performance benchmarking
  
• FinOps and infrastructure cost optimization
  
• High Performance Computing environments
  

Experience Validation

Candidates should be able to explain their direct contribution to AI projects, including:

• AI infrastructure or platforms they designed or managed
  
• GPU and hardware-sizing decisions
  
• Model training or inference environments supported
  
• Performance issues diagnosed and resolved
  
• Improvements achieved in training time, utilization, reliability, or cost
  
• Production AI workloads they deployed or maintained
  

General DevOps, cloud, or system administration experience without direct AI or machine learning exposure will not be sufficient for this position.