Enterprise Architect – Full Stack - Richmond - Axelon Services Corporation

    Axelon Services Corporation
    Axelon Services Corporation Richmond

    6 days ago

    Description

    Job title : Enterprise Architect Full Stack, AI/ML

    Below covers everything you need to know about what this opportunity entails, as well as what is expected from applicants.
    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, , 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. xhuatnn
    • Proven leadership, stakeholder engagement, and enterprise architecture governance experience.

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