Role Description

Lead I - ML Engineering

Who We Are

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 client 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. Visit us at .

You are: 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.

The Opportunity

- 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.

What Are We Looking For

- Preferred Qualifications Bachelor’s/Master’s in Mechanical, Aerospace, Chemical or Materials Engineering, Physics, Applied Mathematics, Computer Science or a related field.
- Solid foundation in 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.

What We Believe We’re proud to embrace the same values that have shaped UST since the beginning. Since day one, we’ve been building enduring relationships and a culture of integrity. And today, it's those same values that are inspiring us to encourage innovation from everyone to champion diversity and inclusion and to place people at the centre of everything we do.

Humility

We will listen, learn, be empathetic and help selflessly in our interactions with everyone.

Humanity

Through business, we will better the lives of those less fortunate than ourselves.

Integrity

We honour our commitments and act with responsibility in all our relationships.

Equal Employment Opportunity Statement

UST is an Equal Opportunity Employer. We believe that no one should be discriminated against because of their differences, such as age, disability, ethnicity, gender, gender identity and expression, religion, or sexual orientation.

All employment decisions shall be made without regard to age, race, creed, colour, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law.

UST reserves the right to periodically redefine your roles and responsibilities based on the requirements of the organization and/or your performance.

- To support and promote the values of UST.
- Comply with all Company policies and procedures

Skills MLOps, Python, PyTorch, ETL

📌 CAE- AI Engineer (Bengaluru)
🏢 UST
📍 Bengaluru