Lead AI Engr

Honeywell Aerospace; Bengaluru; On-site; INR 4,000,000 - 9,000,000; Full time

Job summary

Honeywell Aerospace in Bengaluru seeks a Lead AI Engineer with deep expertise in mechanical design, CFD and structural analysis to accelerate simulation workflows and design exploration. You will translate advanced analytics and AI/ML methods into practical engineering solutions with measurable business impact.

You will build surrogate models, automate workflows, and collaborate with architects, engineers, and product teams to drive innovation across propulsion engines and power systems.

Qualifications

• Master’s degree in mechanical engineering, aerospace engineering or related discipline.
• 12–20 years of experience in mechanical design, CFD, and simulation-led development.
• Strong fundamentals in fluid mechanics, thermodynamics, and solid mechanics.

Responsibilities

• Lead AI/ML-enabled methods for mechanical design, CFD and structural analysis.
• Develop surrogate and physics-informed models for design optimization.
• Automate engineering workflows from data extraction to reporting.
• Collaborate with cross-functional teams to translate analytics into engineering solutions.
• Interpret results, validate models, and recommend design improvements.
• Mentor engineers and promote adoption of AI-enabled methods.

Skills

CFD

FEM

Python

AI/ML in eng

Education

Master’s degree in mechanical/aerospace engineering

Tools

ANSYS Fluent

STAR-CCM+

Abaqus

Nastran

HyperMesh

Job description

Join a company that’s reintroducing itself to the aviation community we’ve helped advance for more than a century. At Honeywell Aerospace (NASDAQ: HONA), we’re launching as an independent, publicly traded aerospace and defense company built on a legacy of operational excellence and mission-focused execution.

Our new brand identity pairs that heritage with real momentum, as we build technology that helps pilots navigate with confidence, aircraft operate more efficiently, and operators stay ahead of change. With our systems on board 90% of the world’s aircraft, your work here has reach that’s rare to find anywhere else.

Focusing on our customers, investing in innovation, and building a culture of accountability and performance is how we’re shaping what comes next.

Every horizon. Every mission. Every day.

Honeywell Aerospace products and services are found on aircraft across commercial aviation, defense and space— from engines and cockpit electronics to cabin systems, mechanical components and connectivity solutions. Our technology helps operators fly more safely, reduce fuel consumption, improve on-time performance and deliver a better experience for the people on board.

As aviation continues to evolve, we’re also developing systems to support autonomous and supersonic platforms, bringing the same focus on safety and efficiency to the next era of flight. With approximately 36,000 employees worldwide and net sales of $17.4B in 2025, Honeywell Aerospace is one of the largest dedicated aerospace companies in the world. Explore our businesses: https://www.honeywellaerospace.com

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.

Key Responsibilities:

• 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.

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.