Quantum Research International is seeking an Intermediate
DevOps / CI/CD Engineer to support the development, deployment, and operation of a secure Retrieval-Augmented Generation (RAG) application that uses Large Language Models (LLMs) for intelligent inference and response generation. This government-contract role is focused on building reliable delivery pipelines, automating development and deployment workflows, and maintaining the infrastructure required to operate AI-assisted applications in secure and potentially disconnected environments. The ideal candidate combines a strong software engineering foundation with practical experience in CI/CD, infrastructure automation, containerized applications, and production operations.
- 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 the deployment and operation of .NET/C#, Go, and Python services across development, test, and production environments.
- Build and maintain containerized application environments using technologies such as Docker, Kubernetes, or comparable platforms.
- Develop infrastructure-as-code and configuration-management solutions to create 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 their supporting services.
- Troubleshoot build, deployment, infrastructure, networking, performance, and availability issues across the delivery lifecycle.
- Improve system reliability, scalability, recoverability, and deployment consistency.
- Support the 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 applicable government security and configuration-management requirements.
Required Skills and Qualifications- Active Top Secret clearance preferred or the ability to obtain and maintain a TS/SCI clearance.
- BS degree in equivalent software engineering software programming/development or computer science engineering field
- Three or more years of professional experience in software development, DevOps, systems engineering, platform engineering, or a related technical field.
- Hands-on experience designing or maintaining CI/CD pipelines using GitLab CI/CD, GitHub Actions, Azure DevOps, Jenkins, or a comparable platform.
- Experience with source-control workflows, branching strategies, pull or merge requests, release management, and automated quality gates.
- Experience deploying and supporting containerized applications using Docker or a comparable container platform.
- Proficiency in at least one automation or scripting language, such as Python, PowerShell, Bash, or Go.
- Working knowledge of software development in at least one of the following languages: .NET/C#, Go, or Python.
- Experience supporting RESTful APIs, microservices, distributed applications, or backend services.
- Familiarity with infrastructure-as-code or configuration-management tools such as 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.
Desired Skills and Qualifications- Experience operating CI/CD pipelines within secure, classified, air-gapped, or otherwise 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 such as 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 such as rolling, blue-green, or canary deployments.
- Familiarity with government security frameworks and controlled environments, including RMF, NIST guidance, 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, such as AWS Certified DevOps Engineer - Professional, Microsoft Certified: DevOps Engineer Expert, Certified Kubernetes Administrator (CKA), Certified Kubernetes Application Developer (CKAD), HashiCorp Certified: Terraform Associate, Red Hat Certified System Administrator (RHCSA), Red Hat Certified Engineer (RHCE), CompTIA Security+, or a comparable cloud, Linux, container, infrastructure-automation, or security certification.
- 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, including model packaging, deployment, monitoring, evaluation, and lifecycle management.
- Experience supporting GPU-enabled workloads or other specialized AI infrastructure.
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