AI Automation Engineer [Remote]

General Dynamics Mission Systems engineers a diverse portfolio of high technology solutions, products and services that enable customers to successfully execute missions across all domains of operation. As an AI Automation Engineer for the AIOps team, you will modernize critical enterprise systems through intelligent automation and AI-powered infrastructure.

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
• 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
• You write code to solve problems — if you're doing something manually more than twice, you're already automating it with GitLab CI, Terraform, Ansible, Kubernetes, or an AI agent
• Deep expertise in end-to-end automation, from CI/CD pipeline design to self-healing infrastructure, container orchestration, and proactive reliability engineering — increasingly augmented by AI-driven decision-making and agentic automation
• Strong understanding of AIOps principles with operational intelligence embedded at every layer — infrastructure-as-code, automated compliance checks, secure pipeline design, and secure AI integration including OAuth-secured MCP servers and least-privilege agent access patterns
• Experience architecting MCP server ecosystems that allow AI agents to securely query databases, trigger deployments, inspect infrastructure state, and interact with third-party APIs — all governed by robust authentication and authorization
• Experience building custom agent skills and tool definitions that enable AI agents to perform complex, multi-step operations across your AIOps toolchain autonomously and reliably
• Experience working in complex, regulated, or mission-critical environments where Kubernetes, Terraform, automated deployments, and AI-augmented operations are the standard, not the exception
• A builder's mindset — you don't maintain the status quo, you transform it with the right tools, the right code, and the right AI agents working alongside your team
• Identifies opportunities to apply AI for continuous improvement and innovation

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