Enterprise Architect

We are seeking a Senior Enterprise Architect to lead the design of enterprise-scale, cloud-native MLOps and data platforms on AWS. This role focuses on architecture strategy, platform governance, and ML lifecycle design"”not hands-on model development or pipeline engineering.The ideal candidate has deep expertise in AWS cloud architecture, MLOps platform design, and enterprise governance frameworks, with the ability to define scalable reference architectures and standards across multiple teams.Key ResponsibilitiesLead enterprise MLOps architecture across the ML lifecycle: ingestion, training, deployment, monitoring, and retrainingDesign scalable AWS-based reference architectures for ML and data platformsDefine governance standards for model lifecycle management, lineage, auditability, reproducibility, and responsible AIArchitect real-time and batch ML inference solutions using microservices and event-driven patternsEstablish CI/CD standards for ML platforms and integrate with enterprise DevOps toolingDrive AWS cloud modernization initiatives including landing zones and multi-account strategiesPartner with engineering, security, and business stakeholders to deliver secure, scalable solutionsProduce architecture artifacts including diagrams, roadmaps, and standards documentationRequired Experience12+ years in software engineering, cloud architecture, or data platforms5+ years as a Solution or Enterprise Architect in AWS environmentsProven experience designing enterprise-scale MLOps or ML platform architecturesStrong background in cloud-native and distributed systems architectureTechnical ExpertiseAWS & Cloud ArchitectureAWS services: EKS/ECS, Lambda, Step Functions, S3, IAM, VPCMulti-account AWS environments and landing zonesInfrastructure as Code (Terraform or CloudFormation)Observability, resiliency, and security architectureMLOps & ML Platform ArchitectureEnd-to-end ML lifecycle architectureModel governance, lineage, and responsible AI controlsML deployment patterns (batch, streaming, real-time)Monitoring, drift detection, and CI/CD for MLCloud-Native SystemsKubernetes, Docker, microservicesEvent-driven architectures (Kinesis, SNS/SQS, EventBridge)Preferred QualificationsExperience with SageMaker, Domino Data Lab, or similar ML platformsMulti-cloud exposure (Azure preferred)TOGAF and/or AWS certificationsSecurity certifications such as CISSP are a plusWhat This Role Is NotNot a Data Scientist or ML Engineer roleNot a DevOps implementation roleNot focused on AIOps or IT operations automation

Created: 2026-05-14