Machine Learning Engineer

3 weeks ago

Mangalore, India Luxoft Full time

Project Description:We are seeking a skilled Machine Learning Engineer to develop and deploy Graph Neural Network (GNN) based surrogate models that approximate complex physics simulations for oil & gas pipeline and well networks. This is a hands-on role for someone who can build high-fidelity neural network models that replace computationally expensive reservoir and network simulators (Nexus, Prosper).Responsibilities:Design and implement Neural Network architectures to model flow dynamics in interconnected pipeline networksBuild surrogate models that accurately predict pressure distributions, flow rates, and network behavior under varying operational scenarios (training data is acquired through running simulations of the physics models)Create data pipelines to extract network topology and simulation results from physics-based models (Nexus/Prosper) and transform them into graph representationsDevelop training frameworks that incorporate physics constraints (conservation laws, pressure-flow relationships) into neural network loss functionsCollaborate with petroleum engineers to ensure model predictions align with physical behavior and operational constraintsImplement model monitoring, validation, and continuous improvement workflowsbusiness trip to KuwaitMandatory Skills Description:Strong expertise in Graph Neural Networks (GCN, GraphSAGE, Message Passing Networks) with proven implementation experienceDeep understanding of deep learning frameworks (PyTorch Geometric, DGL, or TensorFlow GNN)Experience building surrogate models or physics-informed neural networks (PINNs) for engineering applicationsProficiency in Python and scientific computing libraries (NumPy, SciPy, Pandas)Demonstrated ability to work with complex data structures (graphs, time-series, spatial data)Understanding of optimization techniques and handling large-scale training dataTechnical Domain Knowledge:Understanding of graph theory and network analysisExperience with data structures and graph representations (adjacency matrices, edge lists, sparse tensors)Knowledge of hyperparameter tuning, model building and uncertainty quantification in ML modelsNice-to-Have Skills Description:Background in petroleum engineering, process engineering, or fluid dynamicsFamiliarity with reservoir simulation or pipeline hydraulicsExperience with MLOps practices and model lifecycle managementPublications or open-source contributions in graph MLExperience deploying ML models in production cloud environments (containerization, API development)Industry Experience:Oil & gas industry experience is a strong plus, However, candidates with relevant surrogate modeling experience from other engineering domains encouraged to applyEducational Background:MS/PhD in Computer Science, Computational Engineering, Applied Mathematics, or related field preferredStrong mathematical foundation in linear algebra, graph theory, and numerical methodsUnderstanding of graph theory and network analysis

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