AI Engineer

Role Description

At UST, we help the world’s best organizations grow and succeed through transformation. Bringing together the right talent, tools, and ideas, we work with our clients to co-create lasting change. Together, with over 30,000 employees in 25 countries, we build for boundless impact—touching billions of lives in the process.

The Opportunity

• Develop Scientific AI solutions for FEA, CFD, thermal, structural and semiconductor engineering applications.
• Combine physics-based simulation, machine learning and Python automation to build surrogate models, optimization workflows and fast predictive tools that accelerate engineering design and decision-making.
• Develop physics-aware AI/ML models for thermal, structural, fluid-flow and coupled multiphysics problems.
• Design and validate surrogate models, PINNs and operator-learning models such as FNO and DeepONet.
• Automate geometry, meshing, boundary-condition assignment, solver execution, post-processing and data extraction using Python.
• Build scalable DOE and simulation-data pipelines for model training, validation and uncertainty assessment.
• Combine AI prediction with optimization and design-space exploration to identify robust engineering solutions.
• Collaborate with CAE and semiconductor domain experts to define use-cases and physics-based validation criteria.
• Integrate AI workflows with ANSYS, OpenFOAM and internal engineering tools.
• Develop reproducible training, deployment and model-monitoring workflows.

Requirements & Tech Stack

• FEA, heat transfer, fluid mechanics, solid mechanics, numerical methods and multiphysics simulation.
• Hands-on experience with ANSYS, OpenFOAM or similar engineering simulation tools.
• Experience with surrogate modeling, scientific machine learning, PINNs, neural operators, FNO, DeepONet or graph neural networks.
• Strong Python and PyTorch skills, including NumPy, pandas, SciPy and scikit-learn.
• Experience with simulation automation, large engineering datasets, HPC/GPU computing or distributed training.
• Ability to verify and validate models through mesh studies, trusted simulation baselines and experimental correlation.
• MLOps
• ETL

Qualifications

• Bachelor’s/Master’s in Mechanical, Aerospace, Chemical or Materials Engineering, Physics, Applied Mathematics, Computer Science or a related field.