Bu Engineering & Development Professional - Ai Engineer

Organizational interfaces

• Direct Manager

• Functional Manager


• Direct Reports


• Organizational interfaces

• Design Tools and Standardization Manager

• R&D Performance Manager -AI


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• 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.