Bu Engineering & Development Professional - AI Engineer

Work instructions, procedures and information Organizational interfaces Direct Manager Functional Manager Direct Reports Organizational interfaces Design Tools and Standardization Manager R&D Performance Manager -AI Artificial Intelligence Department, IT Manager/team, CAE Metier, DL Metier Competences requirements Knowledge / Qualifications Master’s degree or engineering degree in Mechanics, Physics and/or applied mathematics PhD thesis with application of AI methods would be a plus Previous Experience (If relevant) 3 to 5 years of experience in (automotive) industry Key technical competencies Proven experience in CAE/simulation and optimization tools.

Strong experience with Python programming or equivalent language Have the mathematical knowledge on artificial intelligence / deep learning techniques Proven experience with explicit code for crash analysis or rheology simulation would be a plus Key behavioral competencies (be customer–centric, cultivate innovation, act with audacity, drive results, collaborate, drive engagement, value differences, be resilient, develop yourself) Good command in English Excellent cross-cultural adaptability Self-Task management & reporting Presentation Skills Ability to synthesize and formalize Missions Main Missions Leading and supporting AI projects within the company to enhance the simulation process.

Implementation of deep learning methods applied to Computer Aided Engineering (CAE). Be in close contact with the business end-users of deep learning methods, to support, identify and update their needs. Contribute to the creation of best practices and methodologies. Active monitoring of the scientific state of the art and its implementation in industry.

KPI and/or main deliverables CAE Deep Learning Models Contribution to simulation Process Improvement Indicators Implementation of state-of-the-art industrial AI best practices Activities

1. Data & Simulation Preparation Collect, clean, and prepare CAE datasets (crash, NVH, rheology, CFD…). Work with CAE teams to understand simulation output structures. Ensure data quality, traceability, and compliance with internal standards.
2. Model Development Develop deep learning models for surrogate modelling, prediction, and optimization. Implement model architectures adapted to CAE constraints Train and validate models on internal CAE datasets. Benchmark model performance vs. current CAE processes. Ensure robustness, explainability, and reproducibility.
3. Integration into Engineering Processes Deploy AI models into engineering workflows (tools, scripts, APIs, dashboards). Collaborate with CAE experts to define acceptance criteria. Automate repetitive simulation tasks using AI and Python tooling.
4. Industrialization & Documentation Package models for production (versioning, testing, monitoring). Write technical documentation, guidelines, and best practices. Contribute to internal AI standards and libraries.
5. Collaboration & Support Work closely with CAE, IT, and the central AI/Data Office. Support engineers in using AI models and tools.