Lead AI Engr

• Lead the development and deployment of AI/ML-enabled methods for mechanical design, CFD, structural analysis, and simulation-driven product development.
• Build surrogate models, reduced-order models, physics-informed models, optimization frameworks, and automation workflows for engineering applications.
• Apply AI/ML techniques to improve simulation efficiency, design exploration, predictive capability and engineering decision-making.
• Collaborate with product architects, Chief Engineers, Subject specialists, and cross-functional teams to identify high-value engineering problems and convert them into scalable AI-enabled solutions.
• Interpret CFD and structural analysis results, validate model assumptions, assess technical risks, and recommend design improvements based on data and physics-based insights.
• Automate engineering workflows including data extraction, preprocessing, model training, post-processing, visualization, and report generation.
• Drive adoption of advanced AI/ML engineering methods across teams through technical leadership, mentoring, and stakeholder engagement.
• Ensure solutions are technically robust, explainable, traceable, and aligned with aerospace engineering quality, validation, and business requirements.

Qualifications

Required Qualifications:

• Master’s degree in mechanical engineering, aerospace engineering, applied mechanics, data science, artificial intelligence, or a related discipline from a reputed university.
• 12 to 20 years of relevant experience in mechanical design, CFD, structural analysis, simulation-led product development, engineering automation, or AI/ML-enabled engineering applications.
• Strong fundamentals in fluid mechanics, thermodynamics, heat transfer, turbomachinery, solid mechanics, finite element methods, numerical methods, design optimization, and engineering statistics.
• Hands-on experience with CFD and structural analysis workflows, including model setup, mesh strategy, solver execution, post-processing, validation, and interpretation of results.
• Proven experience applying AI/ML methods to mechanical engineering problems using simulation, experimental, operational, or multi-physics engineering datasets.
• Experience developing surrogate models, reduced-order models, physics-informed machine learning models, response surface models, uncertainty quantification methods, or optimization frameworks.
• Strong programming capability in Python and practical experience with AI/ML libraries such as NumPy, pandas, scikit-learn, TensorFlow, PyTorch, or equivalent platforms.
• Ability to automate engineering workflows for geometry handling, meshing, solver setup, data extraction, model training, post-processing, visualization, and reporting.
• Good understanding of gas turbine engine components, aerospace mechanical systems, propulsion and power system technologies, and simulation-driven product development practices.
• Demonstrated ability to manage multiple technical priorities, deliver development milestones, and work effectively in ambiguous engineering environments.
• Excellent communication, presentation, collaboration, problem-solving, and stakeholder management skills.