Associate Principal - Architecture

AI Platform Architect – GPU, Kubernetes & AI Infrastructure

Experience: 12- 16 Years
Role: AI Platform Architect
Domain: AI Infrastructure, Kubernetes, GPU Platforms, MLOps/GenAIOps
Employment Type: Full-Time

Key Responsibilities

• Design enterprise AI platforms, GPU-enabled Kubernetes environments, and cloud/on-prem landing zones.
• Define platform blueprints, site bootstrap automation, Infrastructure-as-Code, and GitOps operating models.
• Architect multi-tenant AI platforms, tenant onboarding frameworks, and lifecycle management processes.
• Design AI model serving, inference, training, autoscaling, GPU scheduling, and resource-sharing architectures.
• Establish MLOps/GenAIOps frameworks including model registry, CI/CD, observability, and FinOps integration.
• Define HA/DR strategies, cross-region resiliency, failover mechanisms, and recovery workflows.
• Lead architecture reviews, design workshops, and technical governance across engineering teams.
• Act as design authority for AI, Kubernetes, and GPU platform architecture decisions.

Must-Have Skills

Platform & Cloud Architecture

• 12+ years of IT experience with 5+ years in cloud/platform architecture.
• Strong experience designing large-scale cloud and hybrid infrastructure platforms.
• Expertise in landing zones, IAM/KMS, networking, VPC/VNet, hybrid connectivity, and multi-account architectures.
• Professional cloud certification (AWS/Azure/GCP).

Kubernetes & Linux

• Deep Kubernetes architecture and operational experience (EKS, AKS, GKE, OpenShift, On-Prem K8s).
• Strong expertise in multi-tenancy, workload isolation, cluster design, and platform operations.
• Advanced Linux administration, hardening, networking, and troubleshooting (RHEL/CentOS/Ubuntu).

Infrastructure Automation

• Hands-on experience with Terraform or equivalent Infrastructure-as-Code tools.
• Strong knowledge of GitOps, automation pipelines, and platform engineering practices.

AI Platform & GPU Infrastructure

• Experience designing AI training and inference platforms.
• Knowledge of GPU scheduling, workload orchestration, model serving, and inference scaling.
• Exposure to multi-tenant AI platform security, quota management, and workload isolation.

High Availability & Resiliency

• Expertise in HA/DR, multi-region architecture, disaster recovery, failover, and RTO/RPO planning.

Stakeholder Management

• Strong customer-facing experience conducting architecture workshops and executive design reviews.
• Ability to translate business requirements into scalable technical solutions.

Preferred Skills

• NVIDIA AI Enterprise (NVAIE), NIM, Dynamo, RunAI.
• GPU sharing technologies (MIG, Fractional GPU).
• Rafay, Spectro Cloud, vCluster, OPA, Kyverno.
• SageMaker, Vertex AI, Azure ML.
• Telco, Edge, 5G MEC, AWS Outposts, Azure Local.