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Lead AI Engr

Honeywell Aerospace
Bengaluru, IND
Full-time
Lead · 12–20 years experience
Onsite
Discovered 2 days ago
numpypandaspythonpytorchscikit-learntensorflow

Full Job Posting

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

Preferred Qualifications:

  • Experience with commercial and open-source CFD, FEA, and mechanical design tools such as ANSYS Fluent, STAR-CCM+, Abaqus, ANSYS Mechanical, Nastran, HyperMesh, NX, Creo, CATIA, or equivalent platforms.
  • Experience in aircraft propulsion systems, turbomachinery, thermal systems, power systems, or aerospace component development, testing, and certification processes.
  • Familiarity with aerospace design practices, industry standards, and regulatory requirements related to engine design, structural integrity, verification, validation, and certification.
  • Knowledge of machine learning methods such as regression, classification, neural networks, Gaussian processes, Bayesian optimization, variational autoencoders, graph neural networks, reinforcement learning, or generative AI for engineering applications.
  • Exposure to physics-informed neural networks, neural operators, AI-assisted solver acceleration, digital twins, reduced-order models, and simulation-based design optimization.
  • Experience handling large engineering datasets, including data cleaning, feature engineering, model training, validation, error analysis, traceability, and deployment of reusable AI/ML workflows.
  • Familiarity with cloud or HPC environments, version control, model lifecycle management, engineering data management, and collaborative software development practices.
  • Experience mentoring engineers, guiding technical problem solving, and influencing adoption of new AI-enabled engineering methods across teams.
  • What We Value:
  • A strong ownership mindset and ability to lead complex technical initiatives from concept to deployment.
  • Curiosity to adopt emerging AI/ML technologies and apply them responsibly to aerospace engineering challenges.
  • Ability to communicate complex technical concepts clearly to engineering, product, and business stakeholders.
  • A collaborative approach to mentoring, knowledge sharing, and building reusable engineering capabilities.
  • Innovative and creative mindset
  • Ability to manage multiple tasks and projects efficiently, ensuring timely delivery of development milestones.
  • Honeywell Aerospace never requests or accepts money from candidates at any stage of the hiring process. Any demand for payment in exchange for employment opportunities is fraudulent and should be reported immediately to local law enforcement authorities

Responsibilities

  • Lead AI Engineer – Mechanical Design, CFD & Structural Analysis
  • We are looking for a Lead AI Engineer with strong expertise in mechanical design, computational fluid dynamics, and structural analysis to join the Aerospace Advanced & Applied Technology group. In this role, you will develop and deploy AI-enabled engineering solutions that accelerate simulation workflows, improve design exploration, enhance prediction accuracy, and support technology insertion for aircraft propulsion engines, power systems, and related mechanical components. You will work with product architects, simulation specialists, product chiefs, Chief Engineers, and cross-functional teams to translate advanced analytics, AI/ML methods, and physics-based insights into practical engineering solutions that create measurable business value.
  • 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.

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