Senior Engineer / Lead Engineer - Virtual Engineering - AI CAE

GM

2d • Hybrid

About The Position

Senior Engineer / Lead Engineer – AI CAE will Drive AI innovation in CAE analysis. Execute end-to-end projects from idea to deployment, applying Reduced order modelling (ROM), Simulation data driven ML, DoE techniques to solve Manufacturing problems while ensuring data security and delivering measurable impact. Define and deploy process and methods for AI in CAE

Requirements

• Bachelor’s or Masters Degree Mechanical/Automobile/Production /Mechatronics Engineering discipline or similar.
• 5+ years experience in CAE at Automotive Product Development / Manufacturing Engineering.
• 3+ years' experience in implementing AI solutions in CAE
• Should have executed at least 5+ years of Core CAE domain (from problem definition to deployment) experience.
• Strong programming skills in Python, MATLAB, CAE tool-specific APIs (Altair suite, NASTRAN, ANSYS APDL, Abaqus etc.), workflow automation.
• Experience with ML frameworks like Pytorch, TensorFlow.
• Understanding of data annotation tools and MLOps workflows.
• Experience in data handling and feature engineering.
• Strong problem-solving and analytical mindset.
• Experience in domain-specific AI use cases (manufacturing, automotive, etc.).

Nice To Haves

• Knowledge of multi-physics simulations (Structural + thermal + fluid).
• Familiarity with Bayesian optimization and DOE
• Exposure to ML Ops practices for model deployment and monitoring.
• Strong problem-solving mindset and curiosity for AI innovation.
• Ability to translate domain problems into AI solutions.
• Collaboration skills to work with cross-functional teams.
• Clear communication of technical concepts to non-technical stakeholders.

Responsibilities

• Collaborate with stakeholders to understand business problems in the CAE domain and translate them into AI solutions.
• Design, develop, and fine-tune AI/ML models for: Simulation result prediction and design optimization Automating repetitive CAE tasks (meshing, boundary conditions, post-processing).
• Evaluate, validate, and benchmark model performance using appropriate metrics.
• Deploy AI models into production environments in collaboration with IT/AI teams.
• Establish monitoring and maintenance processes to ensure model accuracy over time.
• Ensure that all AI solutions comply with organizational data security, confidentiality, and regulatory requirements.
• Document workflows, results, and lessons learned for organisational knowledge sharing.
• Stay updated on advancements in neural networks, multi-physics simulations, surrogate modelling and physics-informed learning techniques.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees