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Skills Required

ai ml model training design grid mlflow drift automate engineering data lstm python tensorflow pytorch kubernetes tuning maintenance optimization spark compliance

Work Mode

On-site

Job Type

Full Time

Job Description

Responsibilities

  • Architect and build real-time feature pipelines and model training workflows.
  • Design, train, validate, and deploy RUL predictors, anomaly detectors, and LP-based grid solvers.
  • Implement MLOps best practices: MLflow/Kubeflow pipelines, model registry, canary deployments.
  • Collaborate on explainability modules (SHAP, LIME) and drift-detection alerts.
  • Optimize GPU utilization; automate retraining schedules and performance monitoring.


Skills & Experience

  • 3+ years in ML engineering or data science roles; production-grade ML deployments.
  • Expertise in time-series modeling: LSTM, GRU, isolation forest, ensemble methods.
  • Strong Python skills; frameworks: TensorFlow, PyTorch, Scikit-Learn.
  • Experience with Kubernetes-based MLOps: Kubeflow, KServe, MLflow.
  • Proficiency tuning and deploying on NVIDIA GPUs (H100, H200).


Nice-to-Have

  • Domain experience in predictive maintenance, grid optimization, or IIoT.
  • Familiar with feature-store design (TimescaleDB, Feast) and Spark-on-GPU.
  • Knowledge of explainability libraries and regulatory compliance for AI.

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