Associate Principal - Architecture

RR Created for Sachin Anikar

FY27 CBE RAMP UP Q1

AWS AI Services AWS Cloud Architecture

Role Summary

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

Key Responsibilities

AI ML Infrastructure Architecture

Lead the design of endtoend AI infrastructure for model training finetuning inference and monitoring

Architect scalable platforms for

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

MultiCloud Platform Design AWS Azure GCP

Architect cloudagnostic and cloudnative AI platforms across

AWS SageMaker EKS EC2 GPU S3 IAM

Azure Azure ML AKS Azure OpenAI GPU VM series

GCP Vertex AI GKE TPUGPU infrastructure

Enable workload portability resilience and vendor strategy alignment

Define hybrid and multicloud AI operating models

MLOps DevOps Platform Engineering

Establish MLOps frameworks for CICD of ML models pipelines and features

Design automation for

Model lifecycle management

Continuous training and deployment

Monitoring drift detection and observability

Integrate AI pipelines with enterprise DevOps standards

Data Compute Architecture

Design scalable data ingestion feature stores and training datasets

Architect GPUaccelerator strategies NVIDIA GPU TPU inferencing compute

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

Model usage

Data privacy

Responsible AI and compliance requirements

Align AI platforms with enterprise security and regulatory frameworks

Leadership Advisory

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 Platforms

AWS Azure GCP Advanced Architect Level

Cloud networking IAM security and landing zone design

AI ML Platforms

AIML infrastructure design and deployment

Experience with

Azure ML AWS SageMaker GCP Vertex AI

GenAI and LLM platforms training finetuning inference

Infrastructure Platform Engineering

Kubernetes EKS AKS GKE

GPU accelerator infrastructure

Infrastructure as Code Terraform ARM CloudFormation

MLOps Automation

CICD for ML pipelines

Model registry experiment tracking inference scaling

Monitoring logging and performance tuning

Data Systems

Largescale data platforms for ML workloads

Streaming and batch data architectures

Strong understanding of distributed systems

Preferred GoodtoHave Skills

Experience designing enterprise AI platforms at scale

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