AI Engineer

AI Engineer

BU ENGINEERING & DEVELOPMENT PROFESSIONAL - AI ENGINEER

Work instructions, procedures and information Organizational interfaces include Direct Manager, Functional Manager, Direct Reports, Organizational interfaces, Design Tools and Standardization Manager, and R&D Performance Manager - AI. The Artificial Intelligence Department, IT Manager/team, CAE Metier, and DL Metier are involved.

Competences requirements Knowledge / Qualifications: Master’s degree or engineering degree in Mechanics, Physics and/or applied mathematics. A PhD thesis with application of AI methods would be a plus. Previous

Experience

• 3 to 5 years of experience in the automotive industry. Key technical competencies: Proven experience in CAE/simulation and optimization tools; strong experience with Python programming or an equivalent language; solid mathematical knowledge in artificial intelligence and 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 of English and excellent cross-cultural adaptability. Self-task management and reporting, presentation skills, and the ability to synthesize and formalize.

• Missions

• Main missions include leading and supporting AI projects within the company to enhance the simulation process; implementing deep learning methods applied to Computer Aided Engineering (CAE); maintaining close contact with business end-users of deep learning methods to support, identify and update their needs; contributing to the creation of best practices and methodologies; actively monitoring the scientific state of the art and its implementation in industry. KPI and main deliverables include CAE Deep Learning Models, contribution to simulation process improvement indicators, and 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 versus 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.

• Date: Sep 24, 2026

• Location: Pune, IN

• Job Requisition ID: 390618