Pune BU ENGINEERING & DEVELOPMENT PROFESSIONAL AI ENGINEER
- LocationNot stated
- Work typeNot stated
- Posted3 November 2026
- SourceOPmobility's careers page (SAP SuccessFactors)
Live checkFirst seen 3 days ago; still listed on OPmobility's careers page 38 min ago.
At a glance
- Experience3 to 5 years
- EducationMaster's degree
- Skills and toolsPython
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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.
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