Sr Predictive Maintenance Engineer
HybridAtlanta, Georgia, United States
Job Summary
Develop and deliver components of predictive maintenance and asset-reliability systems, including sensor data preparation, feature engineering, model development, and deployment workflows. Build production-grade failure prediction models, equipment health scoring systems, remaining-useful-life estimators, and anomaly detection capabilities that surface actionable recommendations to maintenance and operations teams. Optimize model performance across training efficiency, inference latency, and edge compute constraints while managing the full model lifecycle through MLOps practices. Support quarterly planning, sprint execution, and deployment activities aligned with the APM delivery roadmap and enterprise strategic data outcomes.
Required Qualifications
- Bachelor's degree in Engineering, Computer Science, Data Science, or a related field
- Minimum of 5 years of experience in AI/ML engineering, data science, predictive maintenance, reliability analytics, or industrial AI systems delivery
- Experience developing predictive models using time-series data, sensor data, or equipment telemetry
- Proficiency in Python and ML frameworks
- Familiarity with IoT and OT data
- Strong analytical and problem-solving skills
- Must be legally authorized to work in the United States without the need for current or future sponsorship
Desired Qualifications
- Master's degree or advanced certification in AI, Machine Learning, or a related field
- Experience in manufacturing, industrial, or sustainability-focused organizations
- Familiarity with OT/IoT data, edge computing, or industrial AI deployment environments
- Experience with Novelis or Hindalco technologies and processes
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