Enterprise Architect with AIML

Position: Enterprise Architect with AIML

Location: Reston VA(3 days onsite 2 days Remote)

Role Summary
We are seeking a senior Enterprise Architect to lead the design of cloud-native MLOps and data platforms on AWS. This role is focused on enterprise-scale architecture platform design and governance of the ML lifecycle-not model development or pipeline implementation. The ideal candidate brings deep expertise in AWS cloud architecture and MLOps platform design with the ability to define reference architectures standards and scalable patterns that enable multiple teams to build and operate machine learning solutions in a secure compliant and repeatable manner.

Key Responsibilities

• Define and lead enterprise MLOps architecture across the full ML lifecycle:
  • data ingestion feature engineering training validation deployment monitoring and retraining
• Design cloud-native reference architectures on AWS for ML platforms and data-driven applications
• Establish standards and governance for:
  • model lifecycle management (versioning lineage approvals)
  • reproducibility and environment standardization
  • responsible AI and auditability
• Architect scalable ML inference solutions using microservices and event-driven patterns (batch and real-time)
• Define CI/CD patterns for ML and integrate with enterprise DevOps tooling
• Partner with business and engineering teams as a trusted advisor to translate requirements into scalable architectures
• Lead cloud adoption and modernization strategies including AWS landing zones and multi-account design
• Collaborate with Security Risk and Compliance teams to ensure secure-by-design and compliant architectures
• Produce architecture artifacts (reference architectures diagrams roadmaps)

Required Experience

• 12 years of experience in software engineering data platforms or cloud architecture
• 5 years as a Solution or Enterprise Architect in AWS environments
• Proven experience designing and implementing enterprise-scale MLOps platforms or ML lifecycle architectures
• Strong experience with cloud-native architectures (microservices containerization event-driven systems)
• Hands-on experience with AWS services and infrastructure design

Core Technical Expertise

• MLOps & ML Platform Architecture (Primary Focus)
• End-to-end ML lifecycle architecture (train deploy monitor retrain)
• Model governance: lineage auditability explainability responsible AI controls
• Model deployment patterns: batch real-time and streaming inference
• Monitoring & observability: drift detection data quality performance tracking
• CI/CD for ML and automated deployment pipelines
• AWS Cloud Architecture
• Deep expertise in AWS services such as EKS/ECS Lambda Step Functions S3 IAM VPC
• Experience designing secure scalable multi-account architectures
• Infrastructure as Code (CloudFormation or Terraform)
• Observability logging and resilience patterns
• Cloud-Native & Distributed Systems
• Microservices architecture and container orchestration (Docker Kubernetes)
• Event-driven architecture (Kinesis SNS/SQS EventBridge)
• Service-to-service communication and resiliency patterns
• Data Architecture (Nice to Have)
• Experience with enterprise data platforms (data lakes warehouses streaming)
• Familiarity with real-time and batch data processing systems

Preferred Qualifications

• Experience with enterprise ML platforms (e.g. Domino Data Lab SageMaker or similar)
• Multi-cloud exposure (Azure preferred; GCP is a plus)
• TOGAF or equivalent architecture framework
• AWS Professional Certification (preferred) or Associate level (required)
• Security certifications (e.g. CISSP) are a plus

What This Role Is NOT

• Not a data scientist or ML model development role
• Not a DevOps engineer or pipeline implementation role
• Not focused on AIOps or IT operations automation

Key Skills & Traits

• Strong architectural leadership and decision-making capability
• Ability to define enterprise standards and influence multiple teams
• Excellent communication and stakeholder management skills
• Ability to translate complex concepts into clear architectural artifacts
• Strategic thinking with hands-on technical depth

Education
Bachelors degree in Computer Science Engineering or related field required
Masters degree preferred

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