Role Description Role Overview We are seeking a highly experienced AI Infrastructure Architect to design, build, and govern scalable, secure, resilient, and cost-effective AI platforms across AWS, Microsoft Azure, and Google Cloud Platform. The role will lead end-to-end architecture for AI/ML workloads, including data platforms, model training, fine-tuning, inference, MLOps, GPU infrastructure, observability, security, and governance for enterprise and production-grade use cases. The ideal candidate combines deep cloud infrastructure expertise, hands-on AI/ML platform knowledge, and strong enterprise architecture leadership across multi-cloud environments.

Key Responsibilities
AI/ML Infrastructure Architecture
• Lead the design of end-to-end AI infrastructure for model experimentation, training, fine-tuning, inference, deployment, monitoring, and ongoing operations.
• Architect scalable platforms for batch and real-time ML workloads, LLM-based solutions, Generative AI pipelines, and enterprise AI applications.
• Define standards for model experimentation, versioning, registry, promotion, lifecycle management, and retirement.
• Create reusable reference architectures, blueprints, guardrails, and design patterns for AI workloads.
• Ensure platforms meet scalability, availability, performance, disaster recovery, and operational requirements. Multi-Cloud Platform Design: AWS, Azure & GCP
• Architect cloud-native and cloud-agnostic AI platforms across AWS SageMaker, EKS, EC2 GPU, S3 and IAM; Azure Machine Learning, AKS, Azure OpenAI and GPU VM series; and GCP Vertex AI, GKE and TPU/GPU infrastructure.
• Define workload placement principles based on capability, security, latency, resilience, portability, cost, and strategic vendor alignment.
• Enable workload portability and standardized operating practices across cloud environments.
• Define hybrid and multi-cloud AI operating models, including connectivity, identity, observability, governance, and disaster recovery. MLOps, DevOps & Platform Engineering
• Establish MLOps frameworks for CI/CD and continuous training of models, pipelines, features, and AI applications.
• Design automation for model lifecycle management, experiment tracking, model registry, validation, deployment, rollback, and retraining.
• Implement monitoring for service health, model performance, drift, data quality, latency, throughput, reliability, and cost.
• Integrate AI delivery pipelines with enterprise DevOps, platform engineering, security, and change-management standards.



Data & Compute Architecture
• Design scalable data ingestion, feature stores, training datasets, data lakes, and data access patterns for AI/ML workloads.
• Architect accelerator strategies using NVIDIA GPUs, TPUs, and fit-for-purpose inference compute.
• Optimize utilization, performance, scheduling, capacity, and cost for training and inference workloads.
• Define storage, networking, caching, and distributed-compute patterns for large-scale AI platforms. Security, Governance & Compliance
• Define AI security architecture covering identity, privileged access, data access, network isolation, secrets and key management, encryption, supply-chain security, and tenant/workload isolation.
• Implement governance controls for model usage, data privacy, lineage, approvals, responsible AI, risk management, and compliance.
• Align AI platforms with enterprise security architecture, regulatory obligations, audit requirements, and internal governance frameworks.
• Embed security-by-design, policy-as-code, traceability, and evidence collection into platform workflows. Leadership & Advisory
• Act as the technical authority for AI infrastructure and platform architecture decisions.
• Guide cloud architects, platform engineers, data engineers, ML engineers, security teams, and application teams.
• Support AI platform roadmaps, cloud strategy, capability assessments, investment decisions, and architecture reviews.
• Communicate architectural choices, trade-offs, risks, and recommendations to business, engineering, and leadership stakeholders.
• Mentor teams and promote reusable engineering practices and architecture standards. Core Technical Skills Cloud Platforms & Architecture
• Advanced architecture expertise across AWS, Microsoft Azure, and GCP.
• Strong experience in cloud networking, IAM, security architecture, landing zones, resilience, and multi-cloud governance. AI/ML Platforms
• Hands-on experience designing and deploying enterprise AI/ML infrastructure.
• Expertise with Azure Machine Learning, AWS SageMaker, and GCP Vertex AI.




• Experience with Generative AI and LLM platforms supporting training, fine-tuning, evaluation, inference, and monitoring. Infrastructure & Platform Engineering
• Kubernetes expertise across EKS, AKS, and GKE.
• GPU/accelerator infrastructure architecture, scheduling, performance tuning, capacity management, and cost optimization.
• Infrastructure as Code using Terraform, ARM/Bicep and/or CloudFormation.
• Containerization, platform automation, service mesh, networking, and observability. MLOps & Automation
• CI/CD and continuous training for ML pipelines and AI applications.
• Model registry, experiment tracking, feature/pipeline versioning, deployment automation, and inference scaling.
• Monitoring, logging, ing, drift detection, reliability engineering, and performance tuning. Data Systems
• Large-scale data platforms for AI/ML workloads, including batch and streaming architectures.
• Feature stores, data ingestion, data quality, lineage, governance, and secure data-access patterns.
• Strong understanding of distributed systems and high-performance computing concepts. Preferred Qualifications
• Experience designing and governing enterprise AI platforms at scale.
• Exposure to Responsible AI frameworks, model risk management, and AI governance operating models.
• Strong background in cost optimization and FinOps for GPU-intensive AI workloads.
• Consulting, client-facing advisory, or architecture review experience.
• Experience supporting regulated industries.
• Relevant certifications in AWS, Azure, GCP, Kubernetes, enterprise architecture, security, or AI/ML. Education & Experience
• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related field.
• 12+ years of overall experience in infrastructure, cloud, platform engineering, or enterprise architecture.
• Proven experience leading AI/ML infrastructure architecture initiatives from strategy through production adoption.
• Strong architectural judgement, written and verbal communication, and stakeholder-management capability. Key Competencies
• Enterprise architecture leadership
• Strategic thinking and decision-making
• Multi-cloud architecture and governance
• AI platform engineering and MLOps
• Security, resilience, compliance, and cost management
• Executive communication and stakeholder influence
• Technical mentorship and cross-functional collaboration Job

Location:
Coimbatore

📌 AI Infrastructure Architect (Coimbatore)
🏢 LTM
📍 Coimbatore