AI Infrastructure Architect

Experience: 12+ Years

Summary

Lead the architecture, design, and governance of enterprise-scale AI/ML platforms across AWS, Azure, and GCP. Responsible for AI infrastructure, MLOps, GPU platforms, security, governance, and cloud strategy.

Key Responsibilities

AI/ML Infrastructure
Design infrastructure for:
Model Training
Fine-Tuning
Inference
Monitoring
Build GenAI and LLM-based platforms.
Define reusable AI reference architectures.

Multi-Cloud Architecture
AWS:
SageMaker
EKS
EC2 GPU
S3
IAM
Azure:
Azure ML
AKS
Azure OpenAI
GPU VM Series
GCP:
Vertex AI
GKE
TPU/GPU Infrastructure

MLOps & Platform Engineering
Establish CI/CD for ML.
Design:
Model Lifecycle Management
Continuous Training
Deployment Automation
Drift Detection
Observability

Data & Compute Architecture
Feature Stores
Data Pipelines
GPU Infrastructure
Large-Scale Storage & Networking

Security & Governance
AI Security Architecture
Identity & Access Management
Responsible AI Controls
Compliance & Governance





Leadership
Technical leadership for AI initiatives.
Mentor architects and engineering teams.
Support cloud strategy and platform roadmaps.
Engage executive stakeholders.

Core Technical Skills

Cloud
AWS
Azure
GCP
Cloud Security
IAM
Network Architecture

AI Platforms
Azure ML
SageMaker
Vertex AI
GenAI Platforms
LLM Infrastructure

Infrastructure
Kubernetes (EKS, AKS, GKE)
Terraform
ARM Templates
CloudFormation
GPU Infrastructure

MLOps
CI/CD for ML
Model Registry
Experiment Tracking
Monitoring & Logging

Data Platforms
Distributed Systems
Streaming Architecture
Batch Architecture

Preferred Qualifications
Bachelor's/Master's degree in Computer Science or Engineering.
Experience building enterprise AI platforms at scale.
Solid exposure to Responsible AI and Governance.
Experience in cloud cost optimization and client-facing consulting roles.

📌 Ai Infra Architect Chennai
🏢 LTM
📍 Chennai