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.

Robust 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.

📌 AI Infrastructure and Platform Architect (Indore)
🏢 Ignatiuz
📍 Indore