ML Solution Engineer (Asset Management & Reliability)

Key Responsibilities

Machine Learning & Predictive Solutions

• Develop, evaluate and deploy machine learning and predictive models for industrial asset management applications.
• Apply machine learning techniques such as regression, classification, time-series analysis, anomaly detection and other relevant modelling approaches.
• Develop predictive maintenance and equipment health monitoring solutions for early detection of equipment degradation and potential failures.
• Analyse historical and real-time equipment, sensor and process data to identify patterns, anomalies and failure indicators.
• Evaluate model performance and continuously improve model accuracy and reliability.
• Explore the application of emerging AI technologies, including Generative AI, LLM and RAG, to enhance asset management solutions.

Asset Management & Reliability

• Apply reliability engineering knowledge to support asset performance and predictive maintenance solutions.
• Analyze asset performance using reliability indicators such as MTBF, MTTR, equipment availability and other relevant metrics.
• Apply methodologies such as Reliability Centered Maintenance (RCM), Failure Mode and Effects
• Analysis (FMEA) and Root Cause Analysis (RCA/RCFA) where applicable.
• Work with asset and maintenance data to identify reliability risks and opportunities for performance improvement.
• Support digitalization initiatives involving Asset Operations Management (AOM), Asset Performance Management (APM), condition monitoring and predictive maintenance.

Solution Development & Customer Engagement

• Engage with customers to understand their asset management, reliability and operational challenges.
• Gather and analyse customer requirements and translate them into appropriate ML and digital solution approaches.
• Conduct technical discussions, workshops and solution demonstrations with customers.
• Support solution scoping, feasibility studies, proof-of-concept (PoC) activities and technical proposal development.
• Present analytical findings, ML model results and solution recommendations to customers and key stakeholders.
• Support the implementation and delivery of ML-enabled asset management solutions.

Centre of Excellence & Collaboration

• Work closely with other domain specialists to develop and enhance asset management solutions.
• Provide technical and domain expertise in machine learning, predictive maintenance and asset reliability.
• Contribute to the development of reusable ML models, methodologies, use cases and best practices within the Asset Management CoE.
• Evaluate emerging AI/ML technologies and identify opportunities for application within industrial asset management.
• Support knowledge sharing and capability development across regional teams and stakeholders.

Requirements

• Bachelor's Degree in Computer Science, Data Science, or a related engineering/technical discipline.
• At least 5 years of relevant experience in machine learning, data analytics, reliability engineering, asset management, predictive maintenance or industrial digital solutions.
• Good understanding of machine learning techniques, statistical analysis and predictive modelling.
• Experience with Python and relevant machine learning/data analytics tools and libraries.
• Experience working with industrial equipment, machinery, sensor, process or time-series data would be advantageous.
• Knowledge of asset reliability and maintenance methodologies such as RCM, FMEA, RCA/RCFA, MTBF and MTTR would be advantageous.
• Exposure to Asset Operations Management (AOM), Asset Performance Management (APM), IIoT,
• Digital Twin or condition monitoring technologies would be an advantage.
• Knowledge or experience in Generative AI, LLM, RAG or other emerging AI technologies would be an added advantage.
• Strong analytical and problem-solving skills with the ability to translate business and operational requirements into technical solutions.
• Good communication and presentation skills with the ability to engage customers and collaborate effectively across multidisciplinary teams.

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We regret to inform that only shortlisted candidates will be notified.