AI Automation Engineer [Remote]

General Dynamics Mission Systems is a leading provider of high technology solutions for mission-critical operations. They are seeking an AI Automation Engineer to modernize enterprise systems through intelligent automation and AI-powered infrastructure, focusing on building complete automation solutions and integrating AI agents into the technology stack.

Responsibilities:

• As an AI Automation Engineer for our AIOps team, you'll be a core member of a high-performing, cross-functional team responsible for modernizing critical enterprise systems through intelligent automation, AI-powered infrastructure, agentic AI workflows, and secure, scalable deployment pipelines
• You won't just write scripts — you'll engineer complete automation solutions from the ground up, integrating AI agents, Model Context Protocol (MCP) servers, and intelligent tooling into every layer of the stack

Requirements:

• Requires a Bachelor's degree in Software Engineering, or a related Science, Engineering, Technology or Mathematics field
• Also requires 5+ years of job-related experience, or a Master's degree plus 3 years of job-related experience
• Due to the nature of work performed within our facilities, U.S. citizenship is required
• Proven experience building end-to-end automation solutions using tools like GitLab CI, Kubernetes, Terraform, and Ansible — not just scripting, but full lifecycle design and implementation
• Hands-on expertise deploying and managing containerized applications with Kubernetes and automating infrastructure provisioning with Terraform and Ansible in an AIOps environment
• Experience building and owning CI/CD pipelines end-to-end, leveraging GitLab CI and AI-powered tools to automate testing, deployment, and operational workflows
• Experience designing, deploying, and managing MCP (Model Context Protocol) servers to expose tools, data sources, and APIs as context for AI agents and LLM-powered workflows
• Familiarity with OAuth 2.0 / OpenID Connect authentication flows within MCP servers, including token management, scoped permissions, and secure delegation of access to downstream services
• Hands-on experience building or integrating AI agent skills — defining tool-use capabilities, orchestrating multi-step agentic workflows, and enabling agents to autonomously interact with infrastructure, CI/CD pipelines, and operational systems
• Experience working with or deploying AI/ML models, LLM-based assistants, and agentic frameworks (e.g., Claude Agent SDK, A2A, ACP, or similar) in production or operational environments
• Understanding of prompt engineering, retrieval-augmented generation (RAG), and how to ground AI agents with real-time enterprise context via MCP or similar protocols