AI Infrastructure Architect (AWS AI Services | AWS Cloud Architecture)

LTM; Coimbatore District; On-site; INR 279,000 - 390,600; Full time

Job summary

A leading tech firm in Coimbatore is seeking a highly experienced AI Infrastructure Architect. This role focuses on the design and governance of scalable, secure AI platforms across multicloud environments. Responsibilities include leading AI infrastructure design, establishing MLOps frameworks, and ensuring compliance with security standards. Candidates should have deep cloud expertise and be able to communicate with stakeholders effectively. If you have a strong technical foundation and a passion for AI, we invite you to apply for this exciting role.

Qualifications

• 12 years overall experience in infrastructure, cloud, or platform engineering.
• Proven experience leading AI/ML infrastructure architecture initiatives.
• Strong architectural judgement and stakeholder communication skills.

Responsibilities

• Lead the design of end-to-end AI infrastructure for model training and inference.
• Define reference architectures and reusable patterns for AI workloads.
• Establish MLOps frameworks for CI/CD of ML models and features.
• Design scalable data ingestion and training datasets.

Skills

Cloud networking

IAM security

AI ML Platforms

Infrastructure as Code

MLOps Automation

Education

Bachelor's or Master's degree in Engineering or Computer Science

Tools

Terraform

AWS services

Job description

Overview

We are looking for a highly experienced AI Infrastructure Architect to design build and govern scalable secure and costefficient AI platforms across AWS Azure and GCP. This role will lead the endtoend architecture for AIML workloadsincluding data platforms model training inference MLOps and GPU infrastructuresupporting both enterprise and productiongrade AI use cases.

The ideal candidate combines deep cloud infrastructure expertise handson AIML platform knowledge and enterprise architecture leadership across multicloud environments.

Responsibilities

• Lead the design of endtoend AI infrastructure for model training finetuning inference and monitoring
• Batch and realtime ML workloads
• LLMbased architectures and GenAI pipelines
• Model experimentation versioning and lifecycle management
• Define reference architectures and reusable patterns for AI workloads
• Architect cloudagnostic and cloudnative AI platforms across AWS SageMaker EKS EC2 GPU S3 IAM
• Enable workload portability resilience and vendor strategy alignment
• Define hybrid and multicloud AI operating models
• Establish MLOps frameworks for CICD of ML models pipelines and features
• Continuous training and deployment
• Monitoring drift detection and observability
• Integrate AI pipelines with enterprise DevOps standards
• Design scalable data ingestion feature stores and training datasets
• Optimize performance cost and utilization for AI workloads
• Define storage networking and data access patterns for largescale AI
• Security Governance Compliance
• Define AI security architecture including identity data access secrets management and isolation
• Implement governance controls for Data privacy
• Responsible AI and compliance requirements
• Align AI platforms with enterprise security and regulatory frameworks
• Act as a technical authority for AI infrastructure decisions
• Guide engineering teams cloud architects and ML engineers
• Support AI platform roadmaps cloud strategy and investment decisions
• Engage with stakeholders across business engineering and leadership

Core Technical Skills

• Cloud networking IAM security and landing zone design
• AI ML Platforms
• AIML infrastructure design and deployment
• GenAI and LLM platforms training finetuning inference
• Infrastructure Platform Engineering
• GPU accelerator infrastructure
• Infrastructure as Code Terraform ARM CloudFormation
• MLOps Automation
• CICD for ML pipelines
• Monitoring logging and performance tuning
• Data Systems
• Streaming and batch data architectures
• Strong understanding of distributed systems

Preferred GoodtoHave Skills

• Exposure to Responsible AI frameworks and governance models
• Strong background in cloud cost optimization for AI workloads
• Consulting or clientfacing advisory experience
• Experience supporting regulated industries

Qualifications

• Bachelors or Masters degree in Engineering Computer Science or related field
• 12 years overall experience in infrastructure cloud or platform engineering
• Proven experience leading AIML infrastructure architecture initiatives
• Strong architectural judgement and stakeholder communication skills