Senior Engineer : Virtual Engineering- AI CFD

Sponsorship:

Work Arrangement:

The Role

Senior Engineer / Lead Engineer AI CFD will Drive AI innovation in CFD domain. Execute end-to-end projects from idea to deployment, applying AI/ML knowledge to build surrogate models, automate pre/post-processing, and develop predictive solutions to solve Manufacturing Engineering and process challenges while ensuring data security and delivering measurable impact.

What You'll Do

• Collaborate with stakeholders to understand business challenges in the CFD space and solve them using API based customization and AI methodologies.
• Collect, clean, annotate, and prepare datasets for text analysis and image comparison tasks.
• Design, develop, and fine-tune AI/ML models for:
• Automating mesh generation, solver setup, and post-processing of CFD results
• Building optimization pipelines for thermal and fluid systems using AI-assisted approaches
• 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 organizational knowledge sharing.
• Stay updated on advancements in neural networks, multi-physics simulations, surrogate modelling and physics-informed learning techniques.

Your Skills & Abilities (Required Qualifications)

• Bachelors or Masters Degree Mechanical/Automobile/Production /Mechatronics Engineering discipline or similar.
• 5+ years experience in CFD at Automotive Product Development / Manufacturing Engineering.
• 2+ years experience in implementing AI solutions in CFD
• Should have executed at least 5+ years of Core CFD domain (from problem definition to deployment) experience.
• Strong programming skills in Python and C++ for automation and solver integration.
• Experience with ML frameworks like Pytorch, TensorFlow.
• Knowledge of surrogate modeling, reduced-order modeling (ROMs), and regression techniques.
• Experience in data handling (large-scale CFD datasets) and feature engineering(feature extraction from flow fields like velocity, pressure, turbulence quantities).
• Strong problem-solving and analytical mindset.
• Understanding of data annotation tools and MLOps workflows.
• Experience in domain-specific AI use cases (manufacturing, automotive, etc.).

What Will Give You A Competitive Edge (Preferred Qualifications)

• Exposure to Physics-Informed Neural Networks (PINNs) and Neural Operators for PDE-based learning.
• Familiarity with Bayesian optimization and DOE
• Hands-on with cloud-based CFD simulation platforms.
• 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.

NOTE: This is a 3-year special assignment in enabling AI across Manufacturing workflows.