Enterprise Architect - Full Stack & AI/ML

  • Axelon Services Corporation
  • Richmond, Virginia
  • 02/06/2026
Full time Information Technology Telecommunications Management

Job Description

Job title : Enterprise Architect Full Stack, AI/ML
Location: Richmond, VA

Job Description:

Enterprise Architect Full Stack, AI/ML is responsible for defining and leading enterprise-grade solution architectures that integrate modern full-stack engineering practices with scalable AI/ML capabilities. The role spans application engineering, MLOps, cloud-native architectures, data engineering, and enterprise integration, and requires close collaboration with business, product, engineering, and data science teams. This position requires 12+ years of hands-on and architectural experience in large-scale enterprise environments.



Responsibilities

  • Define end-to-end architecture for full-stack and AI/ML systems across discovery, 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.
  • Architect scalable ML pipelines, automated workflows, CI/CD, and MLOps frameworks.
  • Partner with data scientists and engineers to operationalize AI/ML models with governance, compliance, versioning, and monitoring.
  • Design and review enterprise applications spanning frontend, backend, APIs, microservices, and cloud-native services.
  • Lead multi-cloud and hybrid architectures across AWS, Azure, and GCP, including DevOps, IaC, and observability.
  • Mentor engineering teams and facilitate architecture governance, reviews, and technical audits.


Required Skills

  • Strong full-stack engineering experience with Java, Node.js, Python, Angular/React, REST APIs, microservices, and event-driven architectures.
  • Experience designing and operationalizing AI/ML pipelines, MLOps frameworks, and CI/CD workflows.
  • Deep knowledge of cloud-native architectures, containers (Docker), Kubernetes, and multi-cloud environments (AWS, Azure, GCP).
  • Strong understanding of data engineering, data management, and enterprise integration.
  • Proven leadership, stakeholder engagement, and enterprise architecture governance experience.