Senior Systems Engineer, CAE AIML Integration & Implementation

What you'll do

• Lead the installation, configuration, and integration of AIML software into CAE workflows across on-prem and cloud/HPC environments.
• Integrate CAE tools, data sources, and AIML components with enterprise platforms like Azure, data lakes, and HPC clusters.
• Define and maintain system-level requirements, interfaces, and architecture diagrams for AIML-enabled CAE solutions.
• Develop and execute installation, integration, and regression test plans to validate end-to-end CAE workflow performance and security.
• Harden and scale successful AIML POCs by managing packaging, deployment, monitoring, and support hand-offs.
• Establish operational processes for versioning, configuration management, logging, observability, and incident response for AIML applications.
• Ensure all solutions comply with corporate security, data governance, and responsible AI guidelines.
• Create and maintain user guides, runbooks, and technical documentation to support CAE engineers and IT organizations.

What we're looking for

• Bachelor’s degree in Systems Engineering, Software Engineering, Computer Science, Mechanical/Automotive Engineering, or a related technical field.
• Master’s degree in Engineering, Systems Engineering, Computer Science, or a related field (preferred).
• 7+ years of experience in systems engineering and/or software integration roles, focusing on complex, distributed, or HPC-based systems.
• Hands-on experience installing, configuring, and integrating engineering or scientific software across desktop, HPC, and cloud environments.
• Strong background in systems engineering practices including requirements, interfaces, architecture, validation, and lifecycle management.
• Practical understanding of AI/ML concepts and patterns such as model endpoints, inference services, pipelines, and RAG/agentic workflows.
• Proficiency with scripting and automation using Python, shell, and CI/CD tools for system installation, configuration, and validation.
• Experience with HPC or cloud-based compute environments, including job schedulers, containers, GPU resources, and monitoring tools.
• Demonstrated ability to collaborate with cross-functional teams and communicate complex technical topics to diverse audiences.
• Direct experience with CAE tools, workflows, and integrating commercial AIML/physics-AI tools within engineering environments (preferred).
• Familiarity with GM-like enterprise environments and associated security and compliance expectations (preferred).