Kautex is looking to solve complex engineering problems and enhance simulation, design, and manufacturing workflows through the application of data science, AI, and predictive quality methods.
Requirements
- Strong understanding of FEA and knowledge of comprehensive structural, thermal, dynamic, and multi-physics simulations.
- Knowledge on CAE tools like ANSYS, LS-DYNA & Altair Hyperworks, with an understanding of their application in nonlinear plastics simulations.
- Strong foundation in applied statistics, data analytics, and both supervised and unsupervised learning techniques.
- Foundation in engineering mechanics, materials science, and thermodynamics, knowledge of FEA tools (Ansys/LS-Dyna/Altair) is a strong asset.
- Proficiency in Python and experience with ML libraries.
- Experience with cloud platforms (Azure, AWS), containerization (Docker), and web frameworks (Flask, Django).
- Familiarity with DevOps practices, version control systems (Git), MLOps environments (e.g. Azure ML).
Responsibilities
- Apply data science and statistical methods to solve complex engineering problems and enhance simulation, design, and manufacturing workflows.
- Develop Python-based tools for simulation automation, pre/post-processing, and reporting.
- Design and implement dashboards and data visualization tools to support decision-making.
- Implement intelligent algorithms for pattern recognition and anomaly detection.
- Design and execute DoEs for both virtual simulations and physical testing scenarios.
- Leverage LLMs, autonomous agents, and generative AI tools in engineering context.
- Support digital transformation initiatives through AI/ML integration.
Other
- B.Tech or master's in mechanical engineering, data science, computer science or related field.
- Fluency in English.
- Willingness to travel occasionally (domestic and international).
- Strong analytical and problem-solving skills.
- Ability to handle complex, multidisciplinary engineering challenges.
