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Enterprise Architect
Enterprise ArchitectCynet Systems • Richmond, VA
Enterprise Architect

Enterprise Architect

Cynet Systems • Richmond, VA
[job_card.30_days_ago]
[job_preview.job_type]
  • [job_card.full_time]
[job_card.job_description]

Job Description:
  • The Enterprise Architect will define and govern end-to-end enterprise architecture for full stack and AI/ML platforms.
  • This role drives digital modernization, cloud transformation, and scalable AI-enabled solutions while partnering closely with business and engineering leadership.
Responsibilities:

Enterprise Architecture And Strategy:
  • Define end-to-end architecture for full stack and AI/ML systems across Client, data management, model development, deployment, and operations.
  • Establish enterprise architecture principles, standards, and governance models for AI-enabled platforms.
  • Drive digital modernization and cloud transformation initiatives aligned with business goals.
  • Evaluate emerging technologies including AI/ML, DevOps, MLOps, and cloud platforms to accelerate enterprise innovation.
AI/ML Solution Architecture:
  • Architect scalable ML pipelines, automated workflows, CI/CD pipelines, and MLOps frameworks covering data ingestion, feature engineering, model development, evaluation, deployment, and monitoring.
  • Collaborate with data scientists, ML engineers, and analytics engineers to operationalize AI/ML models at scale.
  • Ensure governance, compliance, versioning, reproducibility, and monitoring of AI/ML systems.
Full Stack And Platform Architecture:
  • Design and review enterprise applications across backend, frontend, cloud APIs, and microservices.
  • Lead architecture for full stack development teams including frontend, middleware, data APIs, microservices, and cloud-native services.
  • Guide teams on performance optimization, caching strategies, distributed systems, and containerized deployments using Docker and Kubernetes.
  • Oversee API-first integration patterns, event-driven designs, and asynchronous architectures.
Cloud And DevOps/MLOps Integration:
  • Architect multi-cloud and hybrid solutions across AWS, Azure, and GCP ensuring interoperability and vendor-neutral design.
  • Lead cloud automation, DevOps pipelines, infrastructure as code, and observability tooling.
  • Implement secure and scalable API management and enterprise connectivity models.
Stakeholder Leadership And Governance:
  • Partner with business leaders to translate business objectives into technical roadmaps.
  • Mentor engineering teams and promote best practices in coding, security, scalability, and architectural rigor.
  • Facilitate architecture review boards, technical audits, and solution governance activities.
Requirement/Must Have:
  • Strong background in full stack engineering including Java, Node.js, Python, Angular or React, REST APIs, microservices, and event-driven architecture.
  • Hands-on experience with AI/ML frameworks such as TensorFlow, Scikit-learn, and MLlib.
  • Deep knowledge of cloud platforms including AWS, Azure, and GCP.
  • Strong experience with containers, Kubernetes, serverless architectures, and distributed systems.
  • Expertise in CI/CD pipelines, DevOps, MLOps, and infrastructure as code using Terraform or CloudFormation.
Experience:
  • 12+ years of overall IT experience.
  • 5-7+ years of experience in solution or enterprise architecture roles.
  • Proven delivery of large-scale enterprise platforms with AI/ML components.
  • Experience leading global, cross-functional, and multi-disciplinary teams.
Skills:
  • Full stack architecture and engineering.
  • AI/ML platform design and MLOps.
  • Cloud and hybrid architecture.
  • Data engineering and real-time data platforms.
  • DevOps, CI/CD, and automation.
Should Have:
  • TOGAF certification.
  • Cloud architect certifications.
  • AI/ML specialization certifications.
  • Experience with enterprise-scale MLOps and federated data science operations.
  • Background in regulated industries such as finance, healthcare, or telecom.
Qualification And Education:
  • Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent work experience.

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