Job Requirements
Chandler, AZ
Secret Polygraph not specified
Mid Level Career (5+ yrs experience)
Salary not specified
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Job Description
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
Join us and be part of a team driving innovative solutions at the forefront of space systems and advanced software!
Your Role and Impact
In this role, you will:
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.
If this job description reads like it was written specifically for you, we’d like to talk with you.
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.
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
Join us and be part of a team driving innovative solutions at the forefront of space systems and advanced software!
Your Role and Impact
In this role, you will:
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.
If this job description reads like it was written specifically for you, we’d like to talk with you.
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.
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