Artificial Intelligence Engineer

12 hours ago

rajkot, India beBeeArtificialIntelligence Full time

Job Opportunity: Artificial Intelligence SpecialistWe are seeking an exceptional Artificial Intelligence Specialist to develop and deploy novel machine learning models that accurately approximate complex physical systems. This is a hands-on role for someone who can build high-fidelity neural network architectures.Design and implement cutting-edge neural network designs to model intricate system dynamics in interconnected networks.Develop predictive models that accurately forecast system 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 and transform them into graph representations.Develop training frameworks that incorporate physical constraints (conservation laws, relationships) into neural network loss functions.Collaborate with engineers to ensure model predictions align with physical behavior and operational constraints.Implement model monitoring, validation, and continuous improvement workflows.Mandatory Skills:Strong expertise in Graph Neural Networks (GCN, GraphSAGE, Message Passing Networks) with proven implementation experience.Deep understanding of deep learning frameworks (PyTorch Geometric, DGL, or TensorFlow GNN).Experience building surrogate models or physics-informed neural networks (PINNs) for engineering applications.Proficiency 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 data.Technical Domain Knowledge:Understanding of graph theory and network analysis.Experience with data structures and graph representations (adjacency matrices, edge lists, sparse tensors).Knowledge of hyperparameter tuning, model building, and uncertainty quantification in ML models.Nice-to-Have Skills:Background in petroleum engineering, process engineering, or fluid dynamics.Familiarity with reservoir simulation or pipeline hydraulics.Experience with MLOps practices and model lifecycle management.Publications or open-source contributions in graph ML.Experience deploying ML models in production cloud environments (containerization, API development).Industry Experience:Oil & gas industry experience is highly valued, however, candidates with relevant surrogate modeling experience from other engineering domains encouraged to apply.Educational Background:MS/PhD in Computer Science, Computational Engineering, Applied Mathematics, or related field preferred.Strong mathematical foundation in linear algebra, graph theory, and numerical methods.Understanding of graph theory and network analysis.

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