AI Platform Engineer

VAE, INC.

  • VA
  • 8 days ago

    Highlights

    This role sits at the intersection of platform engineering, DevSecOps, and security accreditation, serving as the primary technical interface to security engineering, ISSO/ISSM, and authorizing official staff. Design, build, and operate the cloud runtime that agentic AI workloads deploy onto in AWS GovCloud at IL5, including Kubernetes deployment, scaling, observability, and failure recovery.

    Numbers & Facts

    LocationVA

    Description

    VAE, Inc. is seeking an AI Platform Engineer to own the technical accreditation strategy and cloud runtime for the C5ISR program's agentic AI platform. This role sits at the intersection of platform engineering, DevSecOps, and security accreditation, serving as the primary technical interface to security engineering, ISSO/ISSM, and authorizing official staff. The ideal candidate combines deep AWS GovCloud/Kubernetes platform experience with hands-on experience carrying a system through formal ATO or cATO accreditation.

    Key Responsibilities

    • Own the technical accreditation strategy for the program's agentic AI platform, including the control approach for model, tool, and agent-to-agent behavior.
    • Author and maintain the technical artifacts supporting the ATO and cATO package: control narratives, architecture and data flow documentation, boundary definitions, POA&M inputs, and scan evidence.
    • Serve as the primary technical interface to security engineering, ISSO/ISSM, and authorizing official staff, translating agentic AI architecture into terms that support an authorization decision.
    • Design, build, and operate the cloud runtime that agentic AI workloads deploy onto in AWS GovCloud at IL5, including Kubernetes deployment, scaling, observability, and failure recovery.
    • Implement and maintain CI/CD pipelines and infrastructure as code (Terraform, CloudFormation, or comparable) using Platform One and DevSecOps tooling, including container hardening and image accreditation.
    • Integrate Amazon Bedrock and other authorized model endpoints, managing IAM, service quotas, network boundaries, and data flow controls.
    • Instrument agent and tool traffic for logging, tracing, cost attribution, and audit so that automated activity remains observable, attributable, and distinguishable from anomalous behavior.
    • Apply best practices for MLOps, including model and prompt versioning, deployment gating, monitoring, and rollback.
    • Mentor engineers on accreditable design patterns and secure development practices.
    • Develop and execute the platform technical roadmap tied to program and business objectives.

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