AI Infrastructure and Platform Architect
Ignatiuz Inc.
Get more replies from employers
Send a job-specific resume in minutes.
Job summary
Ignatiuz Inc. in Indore (India) seeks an experienced AI Infrastructure and Platform Architect to design, optimize, and manage scalable AI infrastructure across on‑premises, cloud, and hybrid environments.
The role focuses on GPU‑based systems, AI platforms, and production support to ensure performance, reliability, and cost efficiency. The ideal candidate will collaborate with AI/ML developers, DevOps, data engineers, and solution architects to deliver robust AI solutions and scalable platforms.
Qualifications
- Bachelor's or Master's degree in Computer Science, AI, IT, Engineering, or related field.
- Strong experience in AI infrastructure, GPU environments, or production AI workloads.
- Proven ability to design, deploy, and maintain AI platforms across cloud and on‑prem environments.
Responsibilities
- Design and manage AI infrastructure for model training, fine‑tuning, inference, and LLM workloads.
- Define CPU, GPU, RAM, storage, networking, cooling, and power requirements.
- Review hardware and platform performance and recommend upgrades.
- Perform capacity planning to support future workloads.
- Build and maintain AI platforms using Linux, Docker, Kubernetes, and cloud services.
- Configure NVIDIA drivers, CUDA, cuDNN, TensorRT, and related AI acceleration tech.
- Monitor system health, GPU utilization, memory, storage, and network throughput.
- Diagnose infrastructure failures, bottlenecks, and outages.
- Implement monitoring, backup, security, and disaster recovery best practices.
- Prepare architecture documents and runbooks; support production deployment.
Skills
Education
Job description
Ignatiuz is a digital transformation and intelligent workplace consulting company with offices in the US (PA) and India (Indore). Focused on accelerating digital performance through innovation and automation . Our team has worked with a variety of Fortune 500 clients and has a track record of delivering reliable, high-quality solutions that drive business success.
We are seeking an experienced AI Infrastructure andPlatform Architect to design, optimize, and manage scalable AIinfrastructure and platforms across on-premises, cloud, and hybridenvironments.
The ideal candidate should have strong experience withGPU-based systems, AI/ML platforms, infrastructure architecture, performanceoptimization, capacity planning, and production support. The role will workclosely with AI/ML developers, DevOps engineers, data engineers, and solutionarchitects to improve the performance, reliability, scalability, and costefficiency of AI solutions.
- Designand manage AI infrastructure for model training, fine-tuning, inference,computer vision, Generative AI, and LLM workloads.
- DefineCPU, GPU, RAM, VRAM, storage, networking, cooling, and power requirements.
- Reviewexisting hardware and platform performance and recommend upgrades oroptimizations.
- Performcapacity planning to support future workloads and minimize frequenthardware changes.
- Buildand maintain AI platforms using Linux, Docker, Kubernetes, GPUorchestration, and cloud services.
- Configureand manage NVIDIA drivers, CUDA, cuDNN, TensorRT, and related AIacceleration technologies.
- Monitorsystem health, GPU utilization, memory usage, storage performance, andnetwork throughput.
- Diagnoseinfrastructure failures, system crashes, performance bottlenecks, andplatform outages.
- Implementmonitoring, alerting, backup, disaster recovery, security, and operationalbest practices.
- Preparearchitecture documents, hardware specifications, technicalrecommendations, and operational runbooks.
- Supportproduction deployment, troubleshooting, and continuous platformimprovement.
The candidate should also be capable of:
- Reviewingthe end-to-end AI solution and identifying performance, architecture, andinfrastructure gaps.
- Recommendingimprovements to scalability, reliability, maintainability, and costefficiency.
- SupportingAI/ML developers with model training and experimentation environments.
- Helpingreduce training time through GPU optimization, distributed training,resource tuning, and efficient data pipelines.
- Providingguidance on model accuracy, evaluation, hyperparameter tuning, andexperimentation practices.
- Improvingmodel-serving and inference performance.
- Mentoringexisting team members on AI infrastructure and production-readiness bestpractices.
- AIinfrastructure and GPU-based computing
- NVIDIAGPU architecture, CUDA, cuDNN, NCCL, and TensorRT
- PyTorch,TensorFlow, Hugging Face, or similar frameworks
- Cloudand on-premises AI platforms
- Infrastructuresizing and capacity planning
- Performancemonitoring and troubleshooting
- High-performancestorage and networking
- MLOps,CI/CD, automation, and Infrastructure as Code
- Monitoringtools such as Prometheus, Grafana, NVIDIA DCGM, or OpenTelemetry
- Bachelor'sor Master's degree in Computer Science, Artificial Intelligence,Information Technology, Engineering, or a related field.
- Strongoverall experience in infrastructure, cloud, platform engineering,architecture, or AI systems.
- Aminimum of 3 years of direct, hands-on experience specifically workingwith AI infrastructure, machine learning platforms, GPU environments, orproduction AI workloads.
- Strongproblem-solving, troubleshooting, communication, and technicaldocumentation skills.
The candidate's total professional experience may besignificantly higher. However, at least three years should involve genuine,relevant, hands-on work with AI systems and platforms.
Preference will be given to candidates who have experiencewith:
- LargeLanguage Models and Generative AI
- RAGand agentic AI systems
- Computervision and edge AI
- Model-servingplatforms
- AIperformance benchmarking
- FinOpsand infrastructure cost optimization
- HighPerformance Computing environments
- AIinfrastructure or platforms they designed or managed
- GPUand hardware-sizing decisions
- Modeltraining or inference environments supported
- Performanceissues diagnosed and resolved
- Improvementsachieved in training time, utilization, reliability, or cost
- ProductionAI workloads they deployed or maintained
General DevOps, cloud, or system administration experiencewithout direct AI or machine learning exposure will not be sufficient for thisposition.
'/%3e%3cpath%20d='M101.744%2012.4726L102.894%2016.1947L99.0021%2013.2201L101.744%2012.4726ZM105.3%208.40937H100.488L99.0026%203.59473L97.5116%208.41047L92.7%208.40385L96.5965%2011.3834L95.1055%2016.1942L99.0021%2013.2196L101.408%2011.3834L105.3%208.40992V8.40937Z'%20fill='white'/%3e%3cdefs%3e%3clinearGradient%20id='paint0_linear_5593_1381'%20x1='90'%20y1='9.44471'%20x2='99'%20y2='9.44471'%20gradientUnits='userSpaceOnUse'%3e%3cstop%20stop-color='%2300AD70'/%3e%3cstop%20offset='0.998594'%20stop-color='%2300B67A'/%3e%3cstop%20offset='1'%20stop-color='%23DCDCE6'/%3e%3c/linearGradient%3e%3c/defs%3e%3c/svg%3e){kind=small}
