Process Modeling & AI Engineer 

About the Role

We are looking for a Machine Learning & CFD Engineer to join our team for practical, industry-driven projects combining Computational Fluid Dynamics (CFD) and Machine Learning (ML). The engineer will work on building Digital Twin models, reduced-order modeling (ROM), and predictive ML models for chemical engineering and industrial processes.

This role involves not just model development but collaboration with industry clients on real-world challenges, such as temperature field prediction, combustion analysis, and process optimization.

Key Responsibilities

• Preprocess and analyze CFD simulation data for ML workflows.

• Implement PCA, Autoencoders (AE), POD-ROM, and AT-ROM techniques for dimensionality reduction and surrogate modeling.

• Train ML models (MLP, LSTM, regression methods) for prediction of unseen cases.

• Integrate ML models into Digital Twin frameworks for industrial processes.

• Collaborate with clients on data validation, model deployment, and visualization.

Qualifications

• Bachelor’s/Master’s in Chemical Engineering, Mechanical Engineering, or related field.

• Experience (At least 5 years) with CFD tools (ANSYS Fluent) and Python ML libraries (Scikit-learn, TensorFlow, PyTorch).

• Strong foundation in numerical modeling, fluid dynamics, and data analysis.

• Hands-on experience with ROM methods (POD, AE) and predictive modeling preferred.

• Ability to work with cross-functional teams and deliver project milestones.

What We Offer

• Opportunities to work on real industrial projects with leading companies.

• Exposure to cutting-edge digital twin technologies in chemical engineering.

• Flexible project-based engagements with potential for long-term collaboration.

• Competitive remuneration based on skills and project scope.