Experienced Ai/ml Engineer – Predictive Maintenance

Boeing Boeing · Aerospace · Bangalore, India, India

Boeing AI India is seeking an Experienced AI/ML Engineer for Predictive Maintenance to design and implement AI/ML solutions for aircraft prognostics and health management. The role involves developing analytics for aircraft performance, diagnostics, prognostics, operational efficiency, and simulating lifecycle scenarios. The engineer will also support future AI/ML technology development in areas like image processing, NLP, GenAI, and data analytics.

What you'd actually do

  1. Analyze requirements, historical operational data from aircraft fleets, design and prototype innovative solutions to meet diagnostic and prognostic requirements for aircraft sub-systems as per project definition.
  2. Apply Advanced Prognostics & Health Monitoring techniques to assess the state of health, monitor component failures for aircraft systems & subsystems.
  3. Develop and refine algorithms to estimate Remaining Useful Life (RUL) and identify early-stage degradation patterns.
  4. Implement innovative, nonstandard approaches for anomaly detection, fault isolation and health prediction using physics, probabilistic, and machine learning based approaches in high level software (Python, Matlab/Simulink, etc.)
  5. Support project leads to build and productionize ML models (anomaly detection, RUL, fault classification) with modern stacks (TensorFlow, PyTorch, scikit-learn) and MLOps practices (Docker, Kubernetes, model registries).

Skills

Required

  • Python
  • Matlab/Simulink
  • TensorFlow
  • PyTorch
  • scikit-learn
  • Docker
  • Kubernetes
  • time-series databases
  • data lakes
  • Power BI
  • Tableau
  • explainable AI
  • privacy-preserving methods
  • federated learning
  • AI testing
  • verification & validation (V&V)
  • data governance
  • security
  • compliance

Nice to have

  • image/video processing
  • NLP
  • GenAI

What the JD emphasized

  • predictive maintenance
  • diagnostics and prognostics
  • remaining useful life
  • anomaly detection
  • fault isolation
  • health prediction
  • MLOps practices

Other signals

  • predictive maintenance
  • diagnostics and prognostics
  • remaining useful life estimation
  • anomaly detection
  • fault isolation
  • health prediction