We are seeking an AI/ML Engineer with strong fundamentals in mechanical design, computational fluid dynamics, and structural analysis to join the Aerospace Advanced & Applied Technology group. In this role, you will apply machine learning, physics-informed modeling, surrogate modeling, automation, and data-driven engineering methods to accelerate design exploration, simulation workflows, and product technology development for aircraft propulsion engines, power systems, and related components. You will work closely with product architects, simulation specialists, product managers, and Chief Engineers to develop reliable AI-enabled engineering solutions that improve speed, accuracy, and decision-making across mechanical design, CFD, and FEA workflows.
Qualifications and Experience:
- Master’s degree in mechanical engineering, aerospace engineering or related discipline from a reputed university.
- Strong fundamentals in fluid mechanics, thermodynamics, heat transfer, turbomachinery, solid mechanics, finite element methods, numerical methods, and engineering statistics.
- Good understanding of gas turbine engine components, aerospace mechanical systems, and simulation-driven product development.
- Hands-on exposure to CFD, FEA, or mechanical design workflows, including pre-processing, solver execution, post-processing, and interpretation of simulation results.
- Ability to build and validate AI/ML models using engineering simulation data, experimental data, or operational data for design prediction, optimization, classification, anomaly detection, or reduced-order modeling.
- Experience in physics-informed machine learning, surrogate modeling, response surface modeling, reduced-order models, uncertainty quantification, or optimization for engineering applications.
- Working knowledge of Python-based AI/ML development using libraries such as NumPy, pandas, scikit-learn, TensorFlow, PyTorch, or equivalent platforms.
- Ability to automate engineering workflows for geometry handling, mesh generation, solver setup, data extraction, post-processing, and report generation.
- Capability to interpret CFD and structural analysis results, identify key design drivers, and recommend suitable design changes based on simulation and AI/ML insights.
- Exposure to design of experiments, sensitivity studies, parametric analysis, optimization techniques, and statistical validation of model predictions.
- Proven ability to develop new design concepts and translate data-driven insights into practical engineering recommendations.
- Experience in working with geographically distributed stakeholders and cross-functional engineering teams.
- Innovative mindset with curiosity and initiative to adopt emerging AI/ML trends for aerospace design, simulation, and product technology insertion.
- Strong problem-solving skills, attention to detail, ownership mindset, and ability to manage multiple technical tasks.
- Good communication, presentation, interpersonal, and networking skills.
Desired Skills:
- Programming experience in Python; exposure to MATLAB, C++, JavaScript, or other engineering automation platforms is an added advantage.
- Previous project experience implementing AI/ML methods in the mechanical engineering domain is preferred.
- Knowledge of machine learning methods such as regression, classification, neural networks, variational autoencoders, Gaussian processes, Bayesian optimization, graph neural networks, or reinforcement learning for engineering applications.
- Experience with CAD tools such as NX, Creo, CATIA, or equivalent mechanical design platforms. Exposure to commercial CFD software (Fluent, Star CCM+ etc.)
- Exposure to physics-informed neural networks, neural operators, surrogate models, reduced-order models, digital twins, or AI-assisted solver acceleration for CFD and structural analysis.
- Experience handling simulation datasets, including feature engineering, model training, validation, error analysis, and deployment of reusable AI/ML workflows.
- Familiarity with version control, engineering data management, cloud/HPC environments, and collaborative software development practices.
- Strong oral and written communication skills with the ability to explain AI/ML model behavior, assumptions, limitations, and engineering impact to stakeholders at all levels.
- Ability to collaborate effectively with cross-functional teams and communicate technical outcomes clearly to both engineering and non-engineering stakeholders.