System Engineer (AI & Automation), LIGHT

Responsibilities Cross-Disciplinary Automation: Design and implement AI workflows that bridge the gap between Systems Engineering and downstream disciplines (HW, SW, Mech) to ensure seamless requirements traceability and consistency.Physics-Informed Modeling: Develop and deploy Physics-Informed Machine Learning (PIML) models to accelerate simulations in Mechanics and Electrotechnique.AI-Enhanced System Engineering: Integrate AI-assisted coding, automated unit testing, and bug prediction tools into the software development pipeline in compliance with ASPICE standards.Predictive Project Management: Build predictive analytics dashboards for Technical Project Managers to forecast resource bottlenecks, budget risks, and milestone delays using historical project data.Standards & Compliance: Ensure all AI-automated processes and generated outputs adhere to ISO 26262 (Functional Safety) and ISO/SAE 21434 (Cybersecurity) requirements.Toolchain Integration: Lead the integration of AI agents with existing toolchains, including PLM, ALM (Codebeamer), and MATLAB/Simulink.Key competenciesTechnical SkillsAI and Machine Learning Generative AI & LLMs Predictive Modeling Computer VisionData Engineering and Management Data Pipeline Construction: Automating the flow of data into AI models. Data Quality Assurance: Cleaning and labeling datasets to ensure the AI isn't learning from "noise."Process Automation & Integration (MLOps) Workflow Orchestration: Using tools to connect AI outputs to other R&D software (like ELNs or LIMS). Deployment: Ensuring the AI tools are accessible via user-friendly interfaces.Programming & Scripting Python Javascript / Google Apps Script Google Docs / Sheets functions & automation HTTP rest API'sTooling & ALM: ( Plus Knowledge) Codebeamer (Requirement Management Tool) Google Cloud PlatformAutomation & Integration concepts AI & Data Handling: Basic to intermediate experience with AI tools, APIs, or AI agents Interest in applying AI to engineering processesSoft Skills Strong communication and collaboration skills, including ability to translate technical AI capabilities into tangible benefits for the teams Ability to work autonomously and proactively Analytical mindset with strong problem-solving skills User-oriented mindset (training, support, feedback handling) Time ManagmentNice to HaveExperience with requirement traceability or compliance activitiesExposure to AI-assisted automation or low-code/no-code platformsPhysics-Informed Machine Learning (PIML)Predictive Project AnalyticsUnderstanding of system engineering lifecycle and deliverablesAware About Automotive Industry And Embedded Systems.Years of Experience 2 to 3 years experience in AI & tools development