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