Senior Systems Engineer – Predictive Fleet Intelligence

About the role

The Senior Systems Engineer - Predictive Maintenance is the engineering authority for fleet health monitoring and predictive failure detection. The role defines what equipment should be monitored, why it matters, how abnormal behavior is detected, and what actions should follow, translating engineering expertise and operational knowledge into scalable digital solutions.

The position is focused on engineering strategy and requirements rather than hands-on software or analytics development, with success measured by improved fault detection, reduced unplanned downtime, and increased fleet reliability.

Responsibilities

• Fleet Health Monitoring Specifications defining monitoring objectives, required data sources, operating thresholds, engineering logic, and alert behavior.
• Predictive Detection Rule Specifications that enable Software Engineering and Data Science teams to develop automated monitoring applications and predictive analytics.
• Failure Pattern & Leading Indicator Rules Library documenting confirmed failure signatures, detection criteria, engineering rationale, probable causes, and recommended operational responses.
• Predictive Alert Specifications defining alert context, response guidance, escalation logic, notification strategies, and acceptance criteria.
• Fleet Health Dashboard & Operational Intelligence Specifications defining the engineering requirements for dashboards, reports, and digital decision-support products.
• Standardized Alert Response Packages enabling field operations and technical support teams to investigate and resolve predictive alerts without requiring engineering involvement.
• Alert Effectiveness Reports & Continuous Improvement Recommendations measuring monitoring performance, operational outcomes, false-positive trends, and opportunities to improve predictive monitoring capabilities.
• Define the engineering strategy for monitoring the health and performance of complex industrial assets, including control systems, power systems, mechanical equipment, supporting infrastructure, communications networks, and deployed software configurations.
• The author detailed monitoring requirements that define what equipment should be monitored, what operating conditions represent normal performance, and what deviations indicate degradation or impending failure.
• Establish system health logic, engineering thresholds, operating limits, and classification criteria that distinguish healthy operation from abnormal conditions.
• Define data acquisition requirements by identifying the signals, parameters, instrumentation, and operational data necessary to support meaningful equipment health assessment.
• Develop engineering specifications for predictive monitoring capabilities, documenting leading indicators, failure signatures, and detection logic that enable early identification of developing equipment failures.
• Develop equipment-specific monitoring standards and predictive detection strategies for major asset classes across the