ML Engineer

About Gradera Gradera defines a new category of enterprise transformation called Software-Orchestrated Services™ - where software orchestrates human expertise, digital workers, and enterprise systems to deliver governed outcomes at scale. As an AI Native Services firm, we help enterprises redesign how work gets done across operations, product, engineering, customer experience, data, and enterprise workflows to move beyond fragmented AI pilots and disconnected automation toward measurable business outcomes. OVERVIEW We are seeking skilled ML Engineers to join our Simulation & Scenario Enablement team. This is a specialized role at the intersection of machine learning engineering and physics-based simulation. You will design and implement production-grade ML pipelines, build physics-informed neural networks (PINNs) that respect physical constraints, and develop neural architectures that accelerate simulation workloads. You will own the full MLOps lifecycle — from feature engineering and model training to deployment, monitoring, and continuous improvement — ensuring ML models reliably power real-time scenario evaluation and digital twin intelligence. OUR CORE ML ENGINEERING STACK INCLUDES: ML FRAMEWORKS & DEVELOPMENT • PyTorch and TensorFlow for neural network development • Physics-Informed Neural Networks (PINNs) for constraint-aware modeling • Neural ODE solvers (torchdiffeq, diffrax) for continuous-time dynamics • Python (NumPy, SciPy, pandas) for numerical computing MLOPS & PLATFORM • Databricks ML for scalable model training and pipelines • MLflow for experiment tracking, model registry, and deployment • Unity Catalog for ML asset governance and lineage • Delta Lake for feature storage and versioned training data • Feature Store for feature management and serving PRODUCTION & MONITORING • Model serving and inference optimization • Model monitoring, drift detection, and alerting • CI/CD for ML pipelines • Containerized model deployment (Docker, Kubernetes/OpenShift