Predictive Maintenance

Predictive Maintenance & Reliability Lead

Engagement Status: Contract (End of 2028 with possibility of extension)

Overview

We are partnered with a global biopharmaceutical company to support the search for an Predictive Maintenance & Reliability Lead to join its international Engineering and Asset Management function. This is a senior technical role focused on developing and deploying AI-driven predictive and prescriptive maintenance capabilities across manufacturing, laboratories, utilities, facilities and capital projects, combining deep maintenance and reliability expertise with machine learning, industrial data and advanced analytics.

Requirements

• Define and deploy predictive and prescriptive maintenance strategies, standards, governance models and roadmaps across multiple sites.
• Evaluate and implement AI-driven maintenance solutions, machine learning models, predictive analytics platforms and digital twin technologies.
• Assess asset criticality, failure modes and business impact to determine appropriate predictive and prescriptive maintenance approaches.
• Integrate predictive analytics and AI-generated insights into CMMS/EAM systems, maintenance workflows and asset strategies.
• Collaborate with Engineering, Maintenance, Reliability, Data and Digital Technology teams to support industrial data architectures, IoT ecosystems and connected asset environments.

Hard Skills

• Strong hands-on experience with AI-driven predictive and/or prescriptive maintenance solutions.
• Experience applying machine learning, predictive analytics or advanced data-driven methodologies to maintenance and asset-performance use cases.
• Minimum 8 years of relevant experience across reliability, maintenance engineering, condition monitoring, asset management or industrial analytics.
• Strong knowledge of predictive and condition-based maintenance methodologies, including vibration analysis, thermography, ultrasound, lubrication analysis and process analytics.
• Good understanding of industrial data architectures, IoT ecosystems, data lakes, data warehouses and connected asset environments.
• Knowledge of reliability engineering methodologies including FMECA, RCM, RCA and asset criticality analysis.
• Experience with CMMS/EAM platforms and integrating maintenance and asset-performance data into business processes.
• Experience within pharmaceutical, biotechnology or a comparable regulated industrial environment, with knowledge of cGMP, Good Engineering Practices and EHS requirements.
• Experience with Asset Performance Management platforms and Digital Twin technologies would be advantageous.

Soft Skills

• Strong stakeholder management, communication and influencing skills.
• Ability to bridge Engineering, Maintenance, Reliability, Data and Digital Technology teams.
• Strong analytical and structured problem-solving capabilities with a pragmatic approach to technology deployment.
• Ability to work autonomously and provide specialist expertise within a global, multi-site environment.
• Ability to translate asset and business risks into technical requirements, implementation roadmaps and value cases.

Qualifications

• Master’s degree in Mechanical, Electrical, Industrial, Automation, Data or Reliability Engineering, or an equivalent qualification.
• Relevant certifications such as CMRP, CRL, ISO 18436 vibration analysis, thermography, ultrasound, lubrication analysis, IAM or Lean Six Sigma would be advantageous.
• Professional proficiency in English; French or another language used across European sites would be advantageous.