AWS ML Data Architect

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.

THE IMPACT YOU WILL MAKE

This role offers you the flexibility to make each day your own while working alongside teams who care, so you can deliver on these responsibilities:

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.

Qualifications

Education & Experience

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

Enterprise Data & Cloud Architecture

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.

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:

drift detection

data quality checks

outlier detection

performance degradation alerts

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.

Microservice Orchestration & Cloud-Native Engineering

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.

AWS & Cloud Infrastructure Expertise

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.