AI/ML Engineer
Listed on 2026-03-12
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Engineering
Data 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 TeamOur 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.
Job DescriptionWe are looking for an AI/ML Engineer with deep technical expertise and proven leadership in delivering impactful solutions for the oil & gas industry. In this role, you will take part in design, development, and implementation of advanced AI/ML models, working closely with cross-functional teams to optimize operations and deliver data‑driven insights in challenging industrial environments.
Job Overview- Develop dynamic process simulation model to simulate various plant scenarios.
- Exploratory Data Analysis to analyze trends and patterns, data pre‑processing and make intelligent recommendations.
- Implement classical machine learning techniques to prepare soft sensors, reinforcement learning models for process plant autonomous control operations.
- Design and develop AI models that troubleshoot the plant upsets, support asset performance management across various maintenance strategies.
- Leverage Generative AI (Large Language Models, Deep Reinforcement Learning) to enable multi‑agent systems for collaborative decision‑making and autonomous goal‑seeking behavior.
- Ensure AI models are scalable and deployable within industrial platforms, integrating with PLC, DCS, SCADA, Historians, EAM, MES/MOM, SCM, and ERP systems.
- Ensure compliance with ethical AI principles, particularly in terms of fairness, transparency, and bias mitigation.
Technical Leadership & Mentorship
- Develop dynamic process simulation model for various plant scenarios accurately.
- Analyze data historian, exploratory data analysis and data pre‑processing.
- A.I. project implementation from data ingestion and feature engineering to model deployment and monitoring.
- Advocate best practices in data analysis, data pre‑processing and machine learning model development.
- Partner with domain experts, process engineers, and project managers to translate complex operational challenges into AI‑driven solutions.
- Present technical outcomes to both technical and non‑technical audiences, highlighting business value and ROI.
- Bachelor’s/master’s in chemical engineering, AI, Machine Learning, or related field.
- 6+ years of hands‑on experience in AI/ML and process simulations projects execution.
- Proven project delivery experience in industrial or energy sectors, with a preference for oil & gas.
- Demonstrated knowledge of oil & gas processes (upstream, midstream, downstream), instrumentation, and control systems.
- Proven expertise to develop process dynamic simulations using PFDs and P&IDs and trouble shooting.
- Proficiency in handling large‑scale data, time‑series data, and sensor/IoT data within industrial contexts.
- Familiarity with real‑time data challenges and solutions specific to high‑stakes industrial environments.
- Strong foundation in machine learning algorithms (supervised, unsupervised, reinforcement learning), statistical modelling, and optimization techniques.
- Strong experience with classical machine learning, deep learning and reinforcement learning projects.
- Identify relevant metrics for A.I. model evaluation and present technical outcomes to both technical and non‑technical audiences, highlighting business…
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