✦ Luna Orbit — AI & Machine Learning

Machine Learning Engineer

at DXC Technology

📍 2 Locations Hybrid Posted March 13, 2026
Type Not Specified
Experience mid
Exp. Years 0+ years
Education Not specified
Category AI & Machine Learning

This role involves designing, developing, and deploying machine learning models, optimizing performance, and managing ML pipelines in a collaborative environment.

  • Designing ML models
  • Deploying models
  • Optimizing performance
  • Managing ML pipelines
  • Collaborating with data scientists

The technical environment includes Python, ML frameworks like TensorFlow and PyTorch, deployment tools such as ONNX and TensorRT, MLflow, Kubeflow, Azure ML Pipelines, and distributed data processing with PySpark.

The ideal candidate is a motivated machine learning engineer with experience in Python and ML frameworks like TensorFlow and PyTorch. They should be familiar with ML deployment tools, distributed data processing, and have a collaborative problem-solving approach.

PythonML librariesTensorFlowPyTorchMLflowKubeflowAzure ML PipelinesPySparkGitsoftware engineering principles
gradient boosting toolsXGBoostLightGBMCatBoostmodel deploymentONNXTensorRTTensorFlow ServingTorchServe
TensorFlowKerasPyTorchXGBoostLightGBMCatBoostONNXTensorRTTensorFlow ServingTorchServeMLflowKubeflowAzure ML PipelinesPySparkGit
PythonML librariespandasNumPyscikit-learnXGBoostLightGBMCatBoostTensorFlowKerasPyTorchmodel deploymentONNXTensorRTTensorFlow ServingTorchServeMLflowKubeflowAzure ML PipelinesPySparkGitsoftware engineering principlesdistributed data processing
PythonML librariespandasNumPyscikit-learnXGBoostLightGBMCatBoostTensorFlowKerasPyTorchmodel deploymentONNXTensorRTTensorFlow ServingTorchServeMLflowKubeflowAzure ML PipelinesPySparkGitsoftware engineering principlesdistributed data processing
collaborative mindsetproblem-solvingcreativitycontinuous learningteam support
Industry Technology
Job Function Machine Learning Engineer
Clearance Required Candidates must be eligible for clearance
Machine Learning EngineerPythonML librariespandasNumPyscikit-learnXGBoostLightGBMCatBoostTensorFlowKerasPyTorchmodel deploymentONNXTensorRTTensorFlow ServingTorchServeMLflowKubeflowAzure ML PipelinesPySparkGitsoftware engineering principlesdistributed data processingModel deployment

No experience with ML frameworks (TensorFlow, PyTorch), Lack of familiarity with deployment tools (ONNX, TensorRT), Unwillingness to work in a hybrid environment, No experience with distributed data processing (PySpark)

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