AWS ML Data Architect

• Position yourself as a trusted advisor to business teams and partner with them to understand requirements for cloud implementations.
• Provide recommendations for cloud migration and develop technical implementation roadmaps for AWS adoption.
• Create application architecture, data architecture, deployment architectures, functional design specifications, and other technical deliverables.
• Design modern, scalable, secure, and resilient solutions on AWS that meet requirements for availability, performance, and compliance.
• Collaborate with Information Security, Compliance, Controls, and other teams to develop secure and compliant cloud solutions.

As a Cloud and Data Architect, you will be responsible for leading architectural decisions for the Cloud and Data Enterprise Portfolios. You must have a deep understanding of technical architecture and hands-on experience implementing solutions in cloud environments focused on AWS or Azure. You should have a strong understanding of industry best practices around enterprise cloud security, reference architectures, containerization, CI/CD, and cloud-native design patterns.

Experience with microservices architectures, microservice orchestration, and MLOps platforms is a significant advantage. The architect must have experience implementing secure technical and deployment architectures using AWS services, Domino Data Labs, as well as engineering tools that support inter-service communication, data hydration, application security, platform resiliency, model lifecycle management, and enterprise operational governance.

Technical experience across multi-cloud environments (AWS, Azure, GCP) is a plus. Strong interpersonal and communication skills are required., * Bachelor's degree in Computer Science or related field required; Master's degree preferred.

• 12+ years of progressive hands-on experience in application development, analysis, engineering, solution architecture, and technical leadership.
• Minimum 5 years of experience as a solution architect working with AWS or Azure.
• Experience with Architecture principles and the TOGAF framework is a plus.
• AWS Professional Certification preferred; AWS or Azure Architecture Associate Certification required.
• CISSP or equivalent security certification is a plus.

Technical Expertise

• Expertise managing Enterprise Data Platforms including Data Lakes, Data Warehouses, and Data Marts.
• Experience with Real Time and near Real Time data streaming platforms.
• Expertise with relational, semi-structured, and unstructured databases.
• Strong proficiency with Python or Java.
• Experience designing large-scale APIs, microservices, and distributed streaming-based solutions.
• Skilled in supporting and managing large, complex, and geographically distributed cloud environments.
• Strong background in risk assessment, control design, gap remediation, and impact analysis.
• Proficiency with RDS PostgreSQL, Aurora, DynamoDB, and other AWS data services.
• Experience with AWS VPC design, IAM, CloudFormation, AMIs, multi-account strategy, and landing zone architecture.
• Strong knowledge of AWS services such as ELB, ElastiCache, CloudWatch, CloudTrail, S3, Lambda, Kinesis, App Mesh.
• Experience designing cloud logging, alerting, and observability frameworks.
• Expertise in AWS cloud security services and designing secure-by-default architectures.
• Experience with Jenkins, GitHub, Bitbucket, and Docker in DevOps workflows.

MLOps Expertise

Extensive experience with MLOps frameworks and enterprise ML lifecycle automation, including:

• ML Lifecycle Architecture & Automation
• Designing and implementing end-to-end MLOps pipelines: data ingestion, feature engineering, model training, tuning, evaluation, versioning, CI/CD for ML, approvals, and automated deployment.
• Establishing model governance, including lineage, auditability, explainability, data validation, and responsible AI controls.
• Model Deployment, Serving & Monitoring
• Designing microservice-based ML inference architectures using EKS/ECS, Lambda, Step Functions, and event-driven patterns.
• Implementing advanced model monitoring: CloudWatch/OpenTelemetry observability pipelines
• CI/CD for ML (MLOps)
• Building automated ML pipelines using CodePipeline, Bitbucket Pipelines, GitHub Actions, Jenkins, etc., integrated with container registries and SageMaker.
• Defining enterprise patterns for ML environment standardization, reproducibility, and secure deployment.
• Experience with orchestrating microservices using AWS ECS, EKS, Fargate, Lambda, EventBridge, App Mesh, and Step Functions.
• Implementing service mesh patterns for service discovery, traffic routing, observability, and zero-trust communication.
• Designing event-driven architectures leveraging Kinesis, SNS/SQS, and Lambda.
• Experience building highly available, resilient, fault-tolerant cloud architectures.