Security-Cleared AI/ML Automation Engineer — Cyber Ops

ARGO Cyber Systems

Arlington (VA)

Hybrid

USD 150,000 - 190,000

Full time

8 hours ago
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Job summary

Argo Cyber Systems seeks an experienced AI/ML Automation Engineer to architect and implement intelligent automation for cyber operations within a government-facing setting. The role emphasizes ML engineering, generative AI, cloud-native development, and workflow automation to support national cybersecurity missions.

You will collaborate with analysts, data scientists, software engineers, and cloud architects to deploy AI-driven capabilities, integrate LLMs, and build scalable pipelines across

Qualifications

  • Bachelor's degree required in a STEM field.
  • 5+ years of professional AI/ML or data engineering experience.
  • Strong Python development experience.
  • Experience with Databricks AI Platform.
  • Experience integrating LLMs and building AI-driven automation.

Responsibilities

  • Design, develop, deploy AI/ML solutions for cybersecurity operations.
  • Develop intelligent automation using LLMs and autonomous AI agents.
  • Build RAG pipelines for secure knowledge retrieval and decision support.
  • Design prompt strategies and optimize AI model performance.
  • Develop data pipelines for ML training and deployment.
  • Lead AI modernization initiatives and collaborate with stakeholders.

Skills

Python
Autonomous AI agents
LLMs
REST APIs
AWS
Databricks

Education

Bachelor's degree in Computer Science or related STEM

Tools

Databricks AI Platform
Docker
Kubernetes
AWS Bedrock
Git & CI/CD

Job description

Argo Cyber Systems seeks an experienced AI/ML Automation Engineer to architect and implement intelligent automation for cyber operations within a government-facing setting. The role emphasizes ML engineering, generative AI, cloud-native development, and workflow automation to support national cybersecurity missions.

You will collaborate with analysts, data scientists, software engineers, and cloud architects to deploy AI-driven capabilities, integrate LLMs, and build scalable pipelines across

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