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.