Machine Learning Engineer
- Luxoft
- 3 - 7 years
- Mumbai
- 1 month ago
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Job Description
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
Skills
Must have
- 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
- 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
Location - pune,mumbai,chennai,banagalore
Job Classification
Industry: LegalFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Machine Learning EngineerEmployement Type: Full timeContact Details:
Company: LuxoftLocation(s): Mumbai+ View Contact
Keyskills: machine learning scipy mlops python api development data pipelines scientific computing mathematics physics simulations neural networks numpy pandas containerization deep learning tensorflow pytorch network analysis
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