Back
Senior, specialized ML role with metro location and non-Tier1 brand yields moderate competition.
Physics-informed ML and scientific computing focus makes cross-industry transfers difficult.
Explicit 7+ years and specialized physics-ML plus MLOps requirements create strict shortlisting filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and implement physics-informed neural networks (PINNs) and neural ODE solvers to accelerate physics-based simulations and scenario evaluation.
Own the full MLOps lifecycle including feature engineering, scalable ML pipeline development on Databricks, model training, deployment, monitoring, and continuous improvement.
Optimize and govern ML models for real-time digital twin intelligence and ensure robust model serving, drift detection, and CI/CD practices.
7+ years of experience in ML engineering, applied ML, or scientific computing roles.
Master’s or PhD degree in Computer Science, Machine Learning, Computational Science, Physics, or a related field.
Demonstrated experience deploying ML models in production environments at scale.
Proficiency with PyTorch or TensorFlow, physics-based ML techniques, Databricks ML, MLflow, and containerized deployment with Docker/Kubernetes is required.
Strong background in physics-informed or scientific ML with experience in computational physics, numerical methods, or scientific computing.
Experienced in building hybrid ML architectures combining data-driven learning with physics-based priors and neural ODE solvers.
Proven track record working in agile, cross-functional teams delivering ML solutions for digital twin or large-scale simulation platforms, preferably in industrial domains like Manufacturing, Logistics, or Transportation.