Enterprise Architect with AI (Need locals to VA,MD,DC) (Reston)
KMM Technologies, Inc., Reston, VA, United States
Position: Enterprise Architect with AI/ML
Location: Reston, VA - Hybrid - Weekly 3 days Onsite
Duration: Long Term
Job Description:
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
Bachelor’s degree in Computer Science, Engineering, or related field required
Master’s degree preferred
Location: Reston, VA - Hybrid - Weekly 3 days Onsite
Duration: Long Term
Job Description:
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
Bachelor’s degree in Computer Science, Engineering, or related field required
Master’s degree preferred