General DynamicsRemote
DevSecOps Engineer
United States of America
Full Time
1 week ago
$143,000 - $158,000 USD
No H1B
Key skills
GitLab CIAzure Kubernetes ServiceTerraformAnsibleAzure Policy enforcementRole-Based Access ControlContainerized workloadsCI/CD pipelinesStatic Application Security TestingDynamic Application Security TestingDependency scanningModel Context Protocol serversAzure App ServiceAzure Container AppsOAuth 2.0OpenID ConnectMicrosoft Entra IDAI agent integrationAI/ML model deploymentPrompt engineeringRetrieval-augmented generationAIMLLLMClaudeRAGAgenticAzureKubernetesAKSApp ServiceAzure ADEntra IDOAuthGitLabCI/CD
About this role
General Dynamics is a leading company in high technology solutions, products, and services. They are seeking a DevSecOps Engineer to join their AIOps team, responsible for modernizing enterprise systems through secure automation and AI-powered infrastructure.
Responsibilities:
- 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
- Build end-to-end automation solutions using GitLab CI, AKS (Azure Kubernetes Service), Terraform (with AzureRM provider), and Ansible
- Deploy and manage containerized workloads on AKS and automate Azure infrastructure provisioning with Terraform and Ansible in an AIOps environment
- Build and own 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
- Design, deploy, and secure MCP (Model Context Protocol) servers on Azure that 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 using Microsoft Entra ID (Azure AD)
- Build or integrate AI agent skills and enable agents to autonomously interact with Azure infrastructure, GitLab CI pipelines, and operational systems within defined security guardrails
- Work with or deploy AI/ML models, LLM-based assistants, and agentic frameworks in production or operational environments on Azure
- Understand prompt engineering, retrieval-augmented generation (RAG), and how to ground AI agents with real-time enterprise context
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