AI Data Automation Engineer

Albers Aerospace provides aerospace and defense engineering, maintenance, modernization, and sustainment solutions. The AI Data Automation Engineer will develop production-ready automation and AI tools, integrate engineering toolchains and data sources, establish development pipelines, and support engineering teams through documentation, training, and collaboration.

What you'll do:

• Design, develop, and deploy automation and AI tools using cutting-edge technologies in a fast-paced, agile environment
• Write high-quality, maintainable code that meets standards and delivers desired functionality using Python, Java, C++, or other relevant languages
• Build and test integration/automation tools, working with business teams to ensure functional requirements are met
• Apply principles of SDLC, Lean/Agile methodologies, CI/CD, and testing techniques to establish robust development pipelines
• Work with IA and DT teams as necessary to deploy tools in classified spaces
• Integrate with engineering toolchains (Teamcenter, NX, Cameo, Ansys, etc.) through APIs and data structures
• Develop data integration solutions, working with various data sources, databases, and APIs
• Balance delivery speed, quality, and sustainability of tools to support business outcomes at business pace
• Define multigenerational capability roadmaps supporting immediate impact and visionary enablement
• Engage with business teams to understand key process workflows and identify automation opportunities
• Document to-be workflows and define/prioritize integration and automation gaps
• Create comprehensive technical documentation, code comments, and developer guides

What they're looking for:

• Bachelor's Degree in Computer Science, Computer/Electrical Engineering or related STEM discipline
• Must be a U.S citizen. Must possess or be able to obtain a Secret security clearance
• Strong capability in scripting and programming languages (Python, C++, Java, SQL, etc.)
• Proficiency in Windows and Unix/Linux programming environments
• Deep understanding of data concepts, data integration practices, APIs, and web services
• Experience with AWS, containerization (Docker/Kubernetes), and cloud-based solutions
• Knowledge of SDLC methodologies, CI/CD pipelines, and automated testing frameworks
• Version control systems (Git) and collaborative development practices
• Background in engineering capabilities and tools (PLM - Teamcenter, Systems Engineering - Cameo, CAD - NX, etc.) and their data structures and APIs
• Understanding of digital engineering processes, particularly CAD workflows and engineering analysis
• Ability to understand and map business processes, capture requirements, and scope technical solutions
• Prolonged periods sitting at a desk and working on a computer