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.