What are the responsibilities and job description for the Enterprise Architect with AI/ML position at KMM Technologies, Inc.?
3 days onsite @ Reston, VA office.
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
Bachelor’s degree in Computer Science, Engineering, or related field required
Master’s degree preferred