Sr Predictive Maintenance Engineer (APM)

Position Overview

Novelis is one of the world leaders in aluminum recycling and rolling and a leading sustainable aluminum solutions provider. Driven by our purpose of shaping a sustainable world together, we work alongside our customers to provide innovative solutions to the aerospace, automotive, beverage packaging and specialty markets. Headquartered in Atlanta, Georgia, Novelis has approximately 13,000 employees in 32 operating facilities on 4 continents.

Responsibilities & Qualifications

The Sr Predictive Maintenance Engineer supports the design, development, and deployment of AI-driven predictive maintenance and asset-reliability solutions that reduce unplanned downtime and improve equipment reliability across Novelis’ manufacturing operations. Reporting to the Sr AI Engineer Leader of APM, this role delivers and performs predictive maintenance use cases in partnership with Operations, Reliability, Data Engineering, and AI Governance. The role contributes to failure-prediction models, equipment health-monitoring systems, anomaly detection, and remaining-useful-life (RUL) estimators, translating operational and maintenance data into actionable recommendations for maintenance and operations teams—decision support for human action, not autonomous control.

This role is a hands-on engineering position focused on execution, delivery, and continuous improvement of APM solutions. The Sr Predictive Maintenance Engineer works within the technical direction, roadmap, and architecture established by the Sr AI Engineer Leader of APM, helping convert prioritized use cases into reliable production solutions while maintaining engineering quality, model performance, and operational usability.

Capability Alignment

This role is aligned to the APM delivery team within the Decision Intelligence & AI Enablement pillar and contributes to the following enterprise capabilities:

• Predictive Maintenance & Asset Reliability
• Failure Prediction & Remaining Useful Life (RUL) Modeling
• Equipment Health Monitoring & Anomaly Detection
• OT/IoT Sensor Data & Edge Inference for Predictive Models
• MLOps & Model Lifecycle Management for Industrial Systems
• Responsible AI Compliance in Operational Environments (to AI Governance standards)

Responsibilities

Technical Development & Delivery

• Develop and deliver components of predictive maintenance and asset-reliability systems, including sensor data preparation, feature engineering, model development, deployment support, and monitoring workflows.
• Build and improve production-grade failure prediction models, equipment health scoring systems, remaining-useful-life (RUL) estimators, and anomaly detection capabilities that surface prioritized, actionable recommendations to maintenance and operations teams.
• Support the scoping, design, and implementation of industrial AI solutions by applying appropriate modeling approaches, evaluation methods, and deployment patterns under the guidance of the Sr. AI Engineer Leader of APM.
• Optimize model performance across the full stack: training efficiency, inference latency, edge compute constraints, and long-term production stability.
• Contribute to model lifecycle management through MLOps practices such as monitoring, drift detection, retraining support, documentation, and rollback procedures.

Execution Alignment & Enterprise Coordination

• Complete assigned APM work in alignment with Novelis’ enterprise strategic data outcomes, including trusted data, operational reliability, metal flow optimization, 3×30 sustainability goals, and cash focus/operational efficiency.
• Support the enterprise Data & AI Governance framework, ensuring governance is embedded into all workflows and work.
• Support quarterly planning, feature scoping, sprint execution, testing, deployment, and adoption activities aligned to the APM delivery roadmap and critical metric framework.

Accountability Boundaries

This role delivers predictive maintenance models and related engineering components within the roadmap, technical architecture, and technology direction owned by the Sr. AI Engineer Leader of APM. It supports predictions and recommendations for maintenance and operations teams; people—Operations and Reliability—make and complete the maintenance decisions and actions. This role contributes to the APM roadmap, technology strategy, enterprise architecture, autonomous closed-loop control or machine actuation, AI safety and governance standards, core data platforms and ingestion pipelines, business target definitions, performance reporting dashboards, data governance rules, MDM policy, and data access configuration. Where a use case warran