AI Solution Engineer

Not just a job, but a career

Yokogawa, award winner for ‘Best Asset Monitoring Technology’ and ‘Best Digital Twin Technology’ at the HP Awards, is a leading provider of industrial automation, test and measurement, information systems and industrial services in several industries. 

Our aim is to shape a better future for our planet through supporting the energy transition, (bio)technology, artificial intelligence, industrial cybersecurity, etc. We are committed to the United Nations sustainable development goals by utilizing our ability to measure and connect.

About the Team
Our 18,000 employees work in over 60 countries with one corporate mission, to "co-innovate tomorrow". We are looking for dynamic colleagues who share our passion for technology and care for our planet. In return, we offer you great career opportunities to grow yourself in a truly global culture where respect, value creation, collaboration, integrity, and gratitude are highly valued and exhibited in everything we do.

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.
• Analyse asset performance using reliability indicators such as MTBF, MTTR, equipment availability and other relevant metrics.
• Apply methodologies such as Reliability Centred 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 digitalisation 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.

Requirements:

• Bachelor’s degree in engineering, Computer Science, Data Science or a related technical discipline.
• At least 5 years of experience in machine learning, data analytics, reliability engineering, asset management, predictive maintenance or industrial digital solutions.
• Good understanding of machine learning, statistical analysis and predictive modelling, with proficiency in Python and relevant tools.
• Experience with industrial equipment or sensor, process and time-series data is advantageous.
• Knowledge of reliability methodologies (RCM, FMEA, RCA/RCFA, MTBF, MTTR), AOM/APM, IIoT, Digital Twin or condition monitoring is advantageous.
• Exposure to Generative AI, LLMs, RAG or emerging AI technologies is an added advantage.
• Strong analytical and problem-solving skills to translate business needs into technical solutions.
• Good communication, presentation, customer engagement and cross-functional collaboration skills.

By responding to Yokogawa’s advertisement, consent is considered given to Yokogawa to collect the required personal data for the purpose of recruitment with expectation that Yokogawa will protect personal data with security safeguards that are reasonable and appropriate to the sensitivity of the personal data, to protect it from unauthorized access, use or disclosure and complies with applicable regulatory requirements with respect to the retention of personal data.

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

Yokogawa is an Equal Opportunity Employer. Yokogawa wants a diverse, equitable and inclusive culture.  We will actively recruit, develop, and promote people from a variety of backgrounds who differ in terms of experience, knowledge, thinking styles, perspective, cultural background, and socioeconomic status.  We will not discriminate based on race, skin color, age, sex, gender identity and expression, sexual orientation, religion, belief, political opinion, nationality, ethnicity, place of origin, disability, family relations or any other circumstances.  Yokogawa values differences and enables everyone to belong, contribute, succeed, and demonstrate their full potential.

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about our Employee Referral process!