Machine Learning Engineer

2 hours ago

india, IN 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 networks
  • Build 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 representations
  • Develop training frameworks that incorporate physics constraints (conservation laws, pressure-flow relationships) into neural network loss functions
  • Collaborate with petroleum engineers to ensure model predictions align with physical behavior and operational constraints
  • Implement model monitoring, validation, and continuous improvement workflows
  • business trip to Kuwait


Mandatory Skills Description:

  • 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 Description:

  • 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 a strong plus, 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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