Overview

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.