DevOps/CI/CD ENGINEER (SOFTWARE)

Quantum Research International, Inc. (Quantum) provides services and products in cybersecurity, space operations, aviation systems, missile defense, intelligence support, experimentation and test, program management, and audio/visual technology applications. With a corporate office in Huntsville, AL, Quantum hires nationwide and internationally, supporting U.S. Government and warfighter missions from locations including Colorado Springs, CO; Crestview, FL; Orlando, FL; and Tupelo, MS.

About the role

Quantum is seeking an Intermediate DevOps / CI/CD Engineer to support the development, deployment, and operation of a secure Retrieval-Augmented Generation (RAG) application using Large Language Models (LLMs) for intelligent inference and response generation. This government-contract role focuses on building reliable delivery pipelines, automating workflows, and maintaining infrastructure for AI-assisted applications in secure and potentially disconnected environments. The ideal candidate combines software engineering expertise with hands-on experience in CI/CD, infrastructure automation, containerization, and production operations.

Responsibilities

• Design, implement, and maintain CI/CD pipelines for application services, RAG components, AI models, and supporting infrastructure.
• Automate software builds, testing, security scanning, packaging, deployment, rollback, and release management.
• Support deployment and operation of .NET/C#, Go, and Python services across development, test, and production environments.
• Build and maintain containerized application environments using Docker, Kubernetes, or comparable platforms.
• Develop infrastructure-as-code and configuration-management solutions for repeatable, auditable environments.
• Manage build artifacts, container images, application dependencies, secrets, and environment-specific configuration.
• Integrate automated unit, integration, performance, and security testing into delivery pipelines.
• Implement monitoring, logging, alerting, and operational dashboards for AI-assisted applications and supporting services.
• Troubleshoot build, deployment, infrastructure, networking, performance, and availability issues across the delivery lifecycle.
• Improve system reliability, scalability, recoverability, and deployment consistency.
• Support secure delivery of locally hosted or self-hosted LLMs, embedding models, vector databases, and RAG services.
• Collaborate with software developers, AI/ML engineers, cybersecurity personnel, system administrators, and government stakeholders.
• Produce and maintain technical documentation, deployment procedures, operational runbooks, and system configuration records.
• Participate in code reviews, release planning, technical demonstrations, and Agile development activities.
• Ensure delivery processes and operational environments comply with government security and configuration-management requirements.

Requirements

• Active Top Secret clearance preferred or ability to obtain and maintain a TS/SCI clearance.
• BS degree in software engineering, computer science, or equivalent field.
• Three or more years of professional experience in software development, DevOps, systems engineering, or platform engineering.
• Hands-on experience designing or maintaining CI/CD pipelines using GitLab CI/CD, GitHub Actions, Azure DevOps, Jenkins, or comparable platforms.
• Experience with source-control workflows, branching strategies, pull/merge requests, release management, and automated quality gates.
• Experience deploying and supporting containerized applications using Docker or comparable platforms.
• Proficiency in at least one automation or scripting language (Python, PowerShell, Bash, or Go).
• Working knowledge of software development in .NET/C#, Go, or Python.
• Experience supporting RESTful APIs, microservices, distributed applications, or backend services.
• Familiarity with infrastructure-as-code or configuration-management tools (Terraform, Ansible, Helm, or comparable technologies).
• Understanding of Linux-based environments, networking fundamentals, certificates, secrets management, and application configuration.
• Experience integrating automated testing, static analysis, dependency scanning, or security scanning into delivery pipelines.
• Ability to diagnose build failures, deployment issues, service degradation, and environment inconsistencies.
• Understanding of secure software-development and supply-chain practices.
• Ability to work effectively with cross-functional engineering, cybersecurity, and program teams.

Skills

• Experience operating CI/CD pipelines in secure, classified, air-gapped, or disconnected environments.
• Experience with Kubernetes, OpenShift, Rancher, or similar container-orchestration platforms.
• Experience managing private package repositories, container registries, dependency mirrors, or artifact-management platforms.
• Experience with observability technologies (Prometheus, Grafana, OpenTelemetry, Elasticsearch, or comparable tools).
• Knowledge of software supply-chain security, including software bills of materials, artifact signing, provenance, vulnerability management, and dependency governance.
• Experience implementing deployment strategies (rolling, blue-green, or canary deployments).
• Familiarity with government security frameworks and controlled environments (RMF, NIST, FedRAMP, government cloud platforms, or classified enclaves).
• Experience automating system hardening, compliance validation, patching, or configuration auditing.
• Familiarity with high-availability design, backup and recovery, disaster recovery, and operational continuity.
• One or more relevant professional certifications (AWS Certified DevOps Engineer, Microsoft Certified: DevOps Engineer Expert, CKA, CKAD, Terraform Associate, RHCSA, RHCE, CompTIA Security+, or comparable).
• Experience supporting locally hosted or open-weight AI models and their runtime dependencies.
• Familiarity with RAG architectures, embeddings, vector databases, model serving, and AI/ML workflows.
• Experience incorporating model, prompt, dataset, or evaluation versioning into automated delivery processes.
• Familiarity with MLOps practices (model packaging, deployment, monitoring, evaluation, and lifecycle management).
• Experience supporting GPU-enabled workloads or other specialized AI infrastructure.