AI Automation Engineer

Argo Cyber Systems is a Service-Disabled Veteran-Owned Small Business delivering cybersecurity, artificial intelligence, cloud security, digital forensics, threat intelligence, and cyber modernization services to federal agencies and critical infrastructure organizations. The AI/ML Automation Engineer will serve as a senior technical lead designing, developing, deploying, and optimizing AI-driven capabilities for cybersecurity operations, including LLMs, RAG solutions, autonomous agents, cloud-native systems, and workflow automation.

Responsibilities:

• Design, develop, and deploy enterprise AI and machine learning solutions supporting cybersecurity operations
• Develop intelligent automation using LLMs, foundation models, and autonomous AI agents
• Build Retrieval-Augmented Generation (RAG) pipelines for secure knowledge retrieval
• Design prompt engineering strategies and optimize AI model performance
• Develop feature engineering, data preparation, and ML training pipelines
• Support model evaluation, tuning, validation, and continuous improvement
• Design end-to-end automation workflows for cyber operations
• Automate malware collection, detonation, classification, and analysis
• Build AI-driven orchestration supporting incident response and digital forensics
• Develop APIs enabling secure data exchange between cyber platforms
• Automate repetitive analytical processes to improve operational efficiency
• Design cloud-native AI solutions using AWS services
• Develop AI integrations utilizing Amazon Bedrock and foundation models
• Implement scalable ML pipelines using Databricks and cloud data platforms
• Deploy containerized AI workloads using Docker and Kubernetes
• Build CI/CD pipelines supporting rapid AI model deployment
• Design and maintain scalable data ingestion pipelines
• Build data transformation, normalization, and enrichment workflows
• Develop streaming data integrations supporting AI workloads
• Optimize data quality, governance, and operational performance
• Support structured, semi-structured, and unstructured data processing
• Develop AI-enabled malware analysis capabilities
• Support AI-driven threat intelligence enrichment
• Build detection engineering automation
• Develop machine learning capabilities supporting digital forensics
• Assist cyber analysts by integrating AI decision-support capabilities into operational workflows
• Provide technical leadership for AI modernization initiatives
• Mentor engineers on AI engineering best practices
• Collaborate with cybersecurity SMEs and software development teams
• Develop architecture documentation, design artifacts, and technical standards
• Present AI capabilities to executive leadership and government stakeholders