DevSecOps Engineer

General Dynamics is a leading company in high technology solutions, products, and services, and they are seeking a DevSecOps Engineer for their AIOps team. The role involves modernizing enterprise systems through secure automation, AI-powered infrastructure, and integrating security controls into automation solutions.

Responsibilities:

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

Requirements:

• Bachelor's degree in Software Engineering, or related Science, Technology, Engineering or Mathematics field, plus a minimum of 8 years of relevant experience; or Master's degree, plus 6 years relevant 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 GitLab CI, AKS (Azure Kubernetes Service), Terraform (with AzureRM provider), and Ansible — full lifecycle design and implementation with security controls built in from the start, not bolted on
• Hands-on expertise deploying and managing containerized workloads on AKS and automating Azure infrastructure provisioning with Terraform and Ansible in an AIOps environment, with a focus on Azure Policy enforcement, RBAC, and hardened cluster configurations
• Experience building and owning CI/CD pipelines end-to-end — integrating SAST, DAST, dependency scanning, and AI-powered tooling to automate secure testing, deployment, and operational workflows via GitLab CI targeting Azure environments
• Experience designing, deploying, and securing MCP (Model Context Protocol) servers on Azure — leveraging Azure App Service, Container Apps, or AKS — that expose tools, data sources, and APIs as context for AI agents and LLM-powered workflows, with attention to access boundaries and least-privilege exposure
• Hands-on familiarity with OAuth 2.0 / OpenID Connect authentication flows within MCP servers using Microsoft Entra ID (Azure AD), including token lifecycle management, scoped permissions, and secure delegation of access to downstream Azure services
• Experience building or integrating AI agent skills — defining tool-use capabilities, orchestrating multi-step agentic workflows, and enabling agents to autonomously interact with Azure infrastructure, GitLab CI pipelines, and operational systems within defined security guardrails
• 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 on Azure, with awareness of model trust boundaries and output validation
• Understanding of prompt engineering, retrieval-augmented generation (RAG), and how to ground AI agents with real-time enterprise context via MCP or similar protocols — including risks like prompt injection and data leakage, and mitigation patterns applicable to Azure-hosted deployments