Chief Technology Officer (CTO) Physical AI & Computational Fluid Dynamics (CFD)
Job Description
Location: India (Remote / Hybrid)Experience: 14+ Years in Computational Engineering, Aero-thermal Sciences, and Scientific Machine Learning (SciML)
Domain: Renewable Energy, Wind Turbine Aerodynamics, Aerospace Simulations
The Mission
Traditional high-fidelity engineering simulations (CFD, FEA) take days and cost millions in compute time. We are building the next generation of AI-native physics simulation software to provide near-instantaneous, physics-accurate insights for the renewable energy and aerospace sectors.
We are moving past classical brute-force solvers and architecting a scalable enterprise SaaS platform powered by Physics-Informed Neural Networks (PINNs), Fourier Neural Operators (FNOs), and NVIDIA PhysicsNeMo.
As our CTO, you will own the core technical vision, bridge the gap between deep fluid physics and deep learning, and build the engineering engine that transforms cutting-edge SciML research into a scalable commercial product.
What You Will Do
Architect the Core Engine: Design and scale our deep-learning-based surrogate solver pipelines, integrating frameworks like NVIDIA PhysicsNeMo, PyTorch, and custom GPU-accelerated computing layers.
Bridge Physics & AI: Enforce strict conservation laws (Navier-Stokes, continuity equations) into neural network loss functions, ensuring our AI predictions remain physically valid and industry-defensible.
Lead a High-Caliber Team: Recruit, mentor, and lead an elite team of GPU-native developers, applied mathematicians, and deep learning engineers.
Drive Product-Market Fit (PMF): Translate complex scientific architectures into automated, user-friendly enterprise software features that wind farm operators and aerospace OEMs can easily deploy.
Academic & Industry Liaison: Collaborate with top-tier research labs (IISc, IITs) and global renewable energy stakeholders to validate and refine our models against physical wind tunnel and SCADA datasets.
The Profile We Are Hunting For
You are a Physics-First engineer who pivoted into Deep Learning. You understand the math behind traditional finite volume/element methods inside out, but you recognized early on that AI-driven surrogate models are the future of simulation.
The Classical Foundation (7+ Years): Deep expertise in traditional CFD solvers (OpenFOAM, ANSYS Fluent, Star-CCM+), unsteady aerodynamics, turbulence modeling (LES/DES), and mesh generation.
The SciML Pivot (5+ Years): Hands-on experience developing and deploying Physics-Informed Machine Learning models. You are intimately familiar with PINNs, Operator Learning (FNOs, DeepONet), and training physics models on multi-node GPU clusters.
Leadership Pedigree: Proven experience leading technical teams, managing complex codebases, and driving R&D projects from conceptual math to production-grade deployment.
Education: An advanced degree (M.Tech/Ph.D.) in Aerospace, Mechanical, Applied Mathematics, or Computational Science from a premier institution (IITs, IISc, or global equivalent).
Tech Stack You'll Direct
SciML Frameworks: NVIDIA PhysicsNeMo (Modulus), DeepXDE, PyTorch GeometricLanguages & Core Compute: Python, C++, CUDA, WebAssembly (for browser-side rendering)
Classical Solvers (Validation): OpenFOAM, Ansys, or proprietary internal high-fidelity solvers
Visualization/Data: ParaView, WebGL, unstructured mesh processing pipelines
Why Join Us
We are addressing a massive bottleneck in a critical global industry. You won't be tuning ad-click algorithms or building generic wrappers; you will be pushing the absolute boundaries of Physical AI to accelerate the global transition to renewable energy.You will have full technical autonomy, a highly specialized team, and a significant equity stake in the company.
To Apply: Direct message me or email your profile/GitHub to [Confidential Information]. Let's build the future of simulation.- #PhysicalAI #SciML #CFD #DeepLearning #WindEnergy #CTOHiring #NVIDIA #PINNs