Chief Artificial Intelligence Architect

2 weeks ago

agra, India beBeeMachineLearning Full time

Expertise in developing and deploying Graph Neural Network (GNN) based surrogate models is crucial for approximating complex physics simulations.This role involves building high-fidelity neural network models that replace computationally expensive reservoir and network simulators. 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 scenariosCreate data pipelines to extract network topology and simulation results from physics-based models and transform them into graph representationsDevelop training frameworks that incorporate physics constraints 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 workflowsMandatory Skills:Strong expertise in Graph Neural Networks (GCN, GraphSAGE, Message Passing Networks)Deep understanding of deep learning frameworksExperience building surrogate models or physics-informed neural networks for engineering applicationsProficiency in Python and scientific computing librariesDemonstrated ability to work with complex data structures (graphs, time-series, spatial data)Technical Domain Knowledge:Understanding of graph theory and network analysisExperience with data structures and graph representationsKnowledge of hyperparameter tuning, model building and uncertainty quantification in ML modelsNice-to-Have Skills:Background in petroleum engineering, process engineering, or fluid dynamicsFamiliarity with reservoir simulation or pipeline hydraulicsExperience with MLOps practices and model lifecycle managementIndustry 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 methods

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