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AI Platform Engineer

SNVC, LC
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AI Platform Engineer

Primary 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.

Required Qualifications:

  • Bachelor's or Master's degree in Computer Science, Electrical Engineering, Data Science, Mathematics, or a related field.
  • 8+ years of hands-on experience in cloud, platform, DevOps, or MLOps engineering.
  • Active Secret clearance, with the ability to obtain and maintain a Top Secret clearance.
  • Demonstrated experience taking a system through a formal security accreditation, including authoring or substantially contributing to control documentation and interfacing directly with security and authorization stakeholders.
  • Hands-on experience operating in a DoD or federal accredited cloud environment (IL4/IL5, FedRAMP Moderate/High, or comparable).
  • Proficiency in Python and in at least one infrastructure as code technology.
  • Hands-on experience with AWS, container technologies (Docker), and Kubernetes in operational deployments.
  • Demonstrated experience building and maintaining CI/CD pipelines.
  • Working understanding of how ML and LLM workloads are served, scaled, secured, and monitored.
  • Strong collaboration and communication skills, including the ability to defend a technical position to security stakeholders.

Preferred Qualifications:

  • Active Top Secret clearance.
  • Experience working in national security or defense environments.
  • Direct experience with continuous ATO (cATO) and the associated continuous monitoring and pipeline evidence expectations.
  • Hands-on experience with Platform One, Iron Bank, or Big Bang.
  • Experience with AWS Bedrock, Bedrock AgentCore, SageMaker, or comparable managed model services.
  • Experience accrediting or securing systems that use LLMs, autonomous decision logic, or dynamic service discovery.
  • Some exposure to cyber security work (DoD cyber operations, SOC, or security engineering)