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

Role overview

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

We are seeking a highly experienced AI Infrastructure Architect to design, build, and govern scalable, secure, resilient, and cost-efficient 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.

Responsibilities

• AI/ML Infrastructure Architecture !-- [if !supportLists]-->
• !--[endif]--> Lead the design of end-to-end AI infrastructure for model experimentation, training, fine-tuning, inference, deployment, monitoring, and ongoing operations. !-- [if !supportLists]-->
• !--[endif]--> Architect scalable platforms for batch and real-time ML workloads, LLM-based solutions, Generative AI pipelines, and enterprise AI applications. !-- [if !supportLists]-->
• � !--[endif]--> Define standards for model experimentation, versioning, registry, promotion, lifecycle management, and retirement. !-- [if !supportLists]-->
• � !--[endif]--> Create reusable reference architectures, blueprints, guardrails, and design patterns for AI workloads. !-- [if !supportLists]-->
• !--[endif]--> Ensure platforms meet scalability, availability, performance, disaster recovery, and operational requirements.
• Multi-Cloud Platform Design: AWS, Azure & GCP !-- [if !supportLists]-->
• !--[endif]--> 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. !-- [if !supportLists]-->
• !--[endif]--> Define workload placement principles based on capability, security, latency, resilience, portability, cost, and strategic vendor alignment. !-- [if !supportLists]-->
• !--[endif]--> Enable workload portability and standardized operating practices across cloud environments. !-- [if !supportLists]-->
• � !--[endif]--> Define hybrid and multi-cloud AI operating models, including connectivity, identity, observability, governance, and disaster recovery.
• MLOps, DevOps & Platform Engineering !-- [if !supportLists]-->
• � !--[endif]--> Establish MLOps frameworks for CI/CD and continuous training of models, pipelines, features, and AI applications. !-- [if !supportLists]-->
• !--[endif]--> Design automation for model lifecycle management, experiment tracking, model registry, validation, deployment, rollback, and retraining. !-- [if !supportLists]-->
• !--[endif]--> Implement monitoring for service health, model performance, drift, data quality, latency, throughput, reliability, and cost. !-- [if !supportLists]-->
• !--[endif]--> Integrate AI delivery pipelines with enterprise DevOps, platform engineering, security, and change-management standards.
• Data & Compute Architecture !-- [if !supportLists]-->
• !--[endif]--> Design scalable data ingestion, feature stores, training datasets, data lakes, and data access patterns for AI/ML workloads. !-- [if !supportLists]-->
• !--[endif]--> Architect accelerator strategies using NVIDIA GPUs, TPUs, and fit-for-purpose inference compute. !-- [if !supportLists]-->
• � !--[endif]--> Optimize utilization, performance, scheduling, capacity, and cost for training and inference workloads. !-- [if !supportLists]-->
• � !--[endif]--> Define storage, networking, caching, and distributed-compute patterns for large-scale AI platforms.
• Security, Governance & Compliance !-- [if !supportLists]-->
• !--[endif]--> Define AI security architecture covering identity, privileged access, data access, network isolation, secrets and key management, encryption, supply-chain security, and tenant/workload isolation. !-- [if !supportLists]-->
• !--[endif]--> Implement governance controls for model usage, data privacy, lineage, approvals, responsible AI, risk management, and compliance. !-- [if !supportLists]-->
• !--[endif]--> Align AI platforms with enterprise security architecture, regulatory obligations, audit requirements, and internal governance frameworks. !-- [if !supportLists]-->
• !--[endif]--> Embed security-by-design, policy-as-code, traceability, and evidence collection into platform workflows.
• Leadership & Advisory !-- [if !supportLists]-->
• !--[endif]--> Act as the technical authority for AI infrastructure and platform architecture decisions. !-- [if !supportLists]-->
• !--[endif]--> Guide cloud architects, platform engineers, data engineers, ML engineers, security teams, and application teams. !-- [if !supportLists]-->
• � !--[endif]--> Support AI platform roadmaps, cloud strategy, capability assessments, investment decisions, and architecture reviews. !-- [if !supportLists]-->
• � !--[endif]--> Communicate architectural choices, trade-offs, risks, and recommendations to business, engineering, and leadership stakeholders. !-- [if !supportLists]-->
• !--[endif]--> Mentor teams and promote reusable engineering practices and architecture standards.
• Core Technical Skills Cloud Platforms & Architecture !-- [if !supportLists]-->
• !--[endif]--> Advanced architecture expertise across AWS, Microsoft Azure, and GCP. !-- [if !supportLists]-->
• !--[endif]--> Strong experience in cloud networking, IAM, security architecture, landing zones, resilience, and multi-cloud governance.
• AI/ML Platforms !-- [if !supportLists]-->
• !--[endif]--> Hands-on experience designing and deploying enterprise AI/ML infrastructure. !-- [if !supportLists]-->
• !--[endif]--> Expertise with Azure Machine Learning, AWS SageMaker, and GCP Vertex AI. !-- [if !supportLists]-->
• !--[endif]--> Experience with Generative AI and LLM platforms supporting training, fine-tuning, evaluation, inference, and monitoring.
• Infrastructure & Platform Engineering !-- [if !supportLists]-->
• !--[endif]--> Kubernetes expertise across EKS, AKS, and GKE. !-- [if !supportLists]-->
• !--[endif]--> GPU/accelerator infrastructure architecture, scheduling, performance tuning, capacity management, and cost optimization. !-- [if !supportLists]-->
• !--[endif]--> Infrastructure as Code using Terraform, ARM/Bicep and/or CloudFormation. !-- [if !supportLists]-->
• !--[endif]--> Containerization, platform automation, service mesh, networking, and observability.
• MLOps & Automation !-- [if !supportLists]-->
• !--[endif]--> CI/CD and continuous training for ML pipelines and AI applications. !-- [if !supportLists]-->
• !--[endif]--> Model registry, experiment tracking, feature/pipeline versioning, deployment automation, and inference scaling. !-- [if !supportLists]-->
• � !--[endif]--> Monitoring, logging, ing, drift detection, reliability engineering, and performance tuning.
• Data Systems !-- [if !supportLists]-->
• � !--[endif]--> Large-scale data platforms for AI/ML workloads, including batch and streaming architectures. !-- [if !supportLists]-->
• !--[endif]--> Feature stores, data ingestion, data quality, lineage, governance, and secure data-access patterns. !-- [if !supportLists]-->
• !--[endif]--> Strong understanding of distributed systems and high-performance computing concepts.
• Preferred Qualifications !-- [if !supportLists]-->
• !--[endif]--> Experience designing and governing enterprise AI platforms at scale. !-- [if !supportLists]-->
• !--[endif]--> Exposure to Responsible AI frameworks, model risk management, and AI governance operating models. !-- [if !supportLists]-->
• !--[endif]--> Strong background in cost optimization and FinOps for GPU-intensive AI workloads. !-- [if !supportLists]-->
• !--[endif]--> Consulting, client-facing advisory, or architecture review experience. !-- [if !supportLists]-->
• � !--[endif]--> Experience supporting regulated industries. !-- [if !supportLists]-->
• � !--[endif]--> Relevant certifications in AWS, Azure, GCP, Kubernetes, enterprise architecture, security, or AI/ML.
• Education & Experience !-- [if !supportLists]-->
• !--[endif]--> Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related field. !-- [if !supportLists]-->
• !--[endif]--> 12+ years of overall experience in infrastructure, cloud, platform engineering, or enterprise architecture. !-- [if !supportLists]-->
• !--[endif]--> Proven experience leading AI/ML infrastructure architecture initiatives from strategy through production adoption. !-- [if !supportLists]-->
• !--[endif]--> Strong architectural judgement, written and verbal communication, and stakeholder-management capability.
• Key Competencies !-- [if !supportLists]-->
• !--[endif]--> Enterprise architecture leadership !-- [if !supportLists]-->
• !--[endif]--> Strategic thinking and decision-making !-- [if !supportLists]-->
• !--[endif]--> Multi-cloud architecture and governance !-- [if !supportLists]-->
• !--[endif]--> AI platform engineering and MLOps !-- [if !supportLists]-->
• !--[endif]--> Security, resilience, compliance, and cost management !-- [if !supportLists]-->
• !--[endif]--> Executive communication and stakeholder influence !-- [if !supportLists]-->
• !--[endif]--> Technical mentorship and cross-functional collaboration

Employment details

• Job Location: Coimbatore
• Location: Coimbatore [India]