AI/Automation Software Engineer Level 2 with Security Clearance

Indoronix is seeking an : Onsite Job: AI/Automation Software Engineer Level 2 Work location: Chandler Arizona Work Schedule: 9x80 A Bachelor's degree Active Secret Clearance is required to start We are seeking a highly motivated AI / Automation Engineer to join our dynamic team. In this role, You will design, develop, deploy, and support AI-enabled automation solutions that integrate existing LLM and agentic capabilities into engineering and infrastructure workflows. The role emphasizes bringing AI-enabled tools into operational use in on-premise and classified environments. This role sits at the intersection of AI, automation, CI/CD, and infrastructure, giving you the opportunity to:

• Build and integrate AI-enabled automation capabilities into engineering workflows
• Deploy and support LLM-enabled, agentic, and RAG-based solutions
• Enable reliable delivery through containerization, CI/CD, and infrastructure support Your Role and Impact
• Design, develop, document, test, and debug AI-enabled automation capabilities that integrate existing LLM services, APIs, and engineering tools into operational workflows.
• Build, deploy, and support agent orchestration frameworks and Retrieval-Augmented Generation (RAG) solutions, including integrations with vector databases and enterprise data sources.
• Support containerized, on-premise and classified environments at scale, with cloud-based deployment exposure also valued.
• Troubleshoot deployed AI and automation solutions, resolve integration and performance issues, and improve observability, availability, and maintainability over time.
• Develop, deploy, and maintain containerized applications using Docker or comparable container technologies, with attention to load balancing, virtual-machine resource utilization, and system performance.
• Build and maintain CI/CD pipelines, version-control practices, and automated deployment workflows that enable reliable delivery of AI-enabled tools and services.
• Partner with software, DevOps, infrastructure, and mission stakeholders to identify automation opportunities and transition solutions into production use.
• Provide infrastructure support by resolving container, system-performance, resource-utilization, and availability issues that affect deployed solutions.
• Communicate clearly with technical and non-technical stakeholders, both in writing and verbally.
• Perform other related duties as assigned.

Basic Qualifications for Level 2

• Bachelor's degree with 2 years of professional experience - OR - Master's degree with 1 years of professional experience - OR - PhD with 1 year of professional experience.
• Must have the ability to obtain and maintain a U.S. Government DoD Secret security clearance.
• Hands-on experience integrating and deploying LLM-enabled applications, including model hosting, agentic workflows, RAG, and inference considerations. Experience with advanced fine-tuning and inference optimization is preferred and may be developed in the role
• Experience integrating APIs, LLM services, and agent orchestration frameworks into applications or operational workflows. Experience with LangChain, Mastra, or comparable toolchains is welcome
• Experience with Docker or comparable container technologies is required. Experience with orchestration platforms such as Kubernetes or OpenShift is beneficial
• Experience developing, hosting, or supporting AI/ML applications in on-premise containerized environments; cloud-based deployment experience is also valued
• Working knowledge of CI/CD, version control, automated deployment practices, and infrastructure fundamentals. Experience with tools such as GitLab, Jenkins, or similar platforms is preferred
• Hands-on experience working in Linux environments, including scripting and system configuration.
• Proficiency in Python or Go (Golang) with a strong background in software development, debugging, and testing.
• Experience with Retrieval-Augmented Generation (RAG) pipelines and vector databases.
• Strong working understanding of LLM deployment considerations, including hosting, inference, and operational reliability.