AI Platform Architect
Actively Reviewing the ApplicationsGetege EdTech Pvt. Ltd.
India, Karnataka, Bengaluru
Full-Time
On-site
Posted 3 weeks ago
•
Apply by June 14, 2026
Job Description
We are seeking an elite AI Platform Architect to design enterprise-scale, GPU-accelerated AI platforms leveraging NVIDIA technologies, Kubernetes, and cloud-native patterns. Join our AI Center of Excellence to govern centralized MLOps platforms that enable scalable, secure, and reusable AI/ML/GenAI development across hybrid cloud environments.
Role Summary
The AI Platform Architect owns the design and governance of centralized platforms enabling scalable AI operations. You'll architect NVIDIA GPU-accelerated reference architectures, establish Kubernetes-based MLOps standards, implement model lifecycle governance, and drive enterprise-wide AI platform security, performance optimization, and cost efficiency.
Key Responsibilities
AI Platform Architecture
Role Summary
The AI Platform Architect owns the design and governance of centralized platforms enabling scalable AI operations. You'll architect NVIDIA GPU-accelerated reference architectures, establish Kubernetes-based MLOps standards, implement model lifecycle governance, and drive enterprise-wide AI platform security, performance optimization, and cost efficiency.
Key Responsibilities
AI Platform Architecture
- Architect enterprise-scale, GPU-accelerated AI platforms using NVIDIA technologies, Kubernetes, and cloud-native patterns
- Define cloud-native AI platform standards for consistent deployment across private/public/hybrid clouds
- Design AI model lifecycle management (versioning, validation, governance, observability) on GPU-backed platforms
- Establish containerization standards (Docker/Kubernetes) ensuring GPU utilization and MLOps integration
- Partner with security/legal teams to enforce AI platform security standards (GPU isolation, IAM, encryption, audit logging)
- Monitor GPU/compute utilization and drive cost-optimization/capacity planning strategies
- Mitigate architectural/performance risks for enterprise-scale AI deployments
- Serve as technical advisor to AI CoE leadership, aligning NVIDIA GPU infrastructure with business strategy
- Collaborate with data scientists, MLOps engineers, and app teams for seamless AI integration
- Continuously optimize GPU utilization, inference latency, and training throughput
- Provide platform health, performance, and cost reports to stakeholders
Required Skills
Leadership
Business Strategy
Training
Audit
Docker
Kubernetes
IAM
MLOps
Encryption
Validation
Governance
Performance optimization
Capacity Planning
Technical leadership
Platform architecture
Lifecycle Management
NVIDIA
Hybrid Cloud
Logging
GPU
Cloud environments
Model lifecycle management
Inference
Model Lifecycle
Compute
GenAI
Legal
Observability
AI/ML
AI integration
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