The mission of this job is to lead the development and integration of Agentic AI to accelerate product development with the help of Modeling and Simulations. Developed AI tools will disseminate Modeling and simulation capabilities within Entegris and hence provide a strong platform to competitively position Entegris in the market.
The Lead AI Engineer will build Agentic AI and ML surrogates that automate simulation workflows, generate synthetic data at scale, deliver instant simulation predictions, and enable inverse design.
Key Responsibilities
- Architect workflows for end-to-end automation of Modeling & Simulation steps via an agentic AI interface
- Create framework for generating large and labeled synthetic datasets produced from automated parametric sweeps and DOE plans. Prepare data for rapid Machine Learning (ML) experimentation
- Choose and develop appropriate ML surrogates for Entegris products to deliver instant estimates versus full physics runs
- Develop inverse design methods to translate customer specs into optimized design candidates using surrogate models
- Demonstrate significant time savings by reducing developed workflow durations from days to minutes for at least one workflow, each validated against experimental data with 90% accuracy
- Discuss, propose and present results to engineering and management teams
Requisite Criteria & Skills
- MS or PhD preferred in Computer science, Mechanical Engineering, Aerospace Engineering, Chemical Engineering, Materials Science or Applied Physics
- Strong working knowledge of Machine Learning techniques relevant to engineering data that include regression based surrogate modeling, Neural networks (e.g., CNNs, GNNs) where applicable to geometry or field data
- Demonstrated experience in preparing simulation data for rapid ML experimentation and developing ML surrogate models for instant predictions
- Experience in developing or applying inverse design methods
- Hands on experience in designing agentic AI systems or complex AI driven workflows, including multi step goal-oriented task execution, orchestration of multiple specialized components (agents, tools, APIs)
- Strong foundation in physics based Modeling & Simulation. Expert level use of CFD and FEM simulations specific to computational tools like Ansys, Comsol, Abaqus and or opensource tools like openFoam is required
- Strong proficiency in Python /C/C++ for simulation software API's and handling large simulation outputs
- Demonstrated experience in validating AI/ML outputs against experimental or trusted reference data
- Any domain knowledge relating to Multiphysics, multiphase and porous flows is an advantage
- Strong communication, ability to interact with personnel with different levels of experience and backgrounds
- Contribute positively to a teamwork environment
