Industrial AI/ML Engineer (Predictive Maintenance & Generative AI)

Job Description Design and develop AI models such as Predictive Maintenance (PdM), Process Optimization, and Operational Efficiency, especially in asset-intensive industries – Oil & Gas, Mining, and Utilities. Build and train machine Learning models (supervised, unsupervised learning, classification, regression) with diagnostic, predictive and prescriptive analytics. Build industrial knowledge/virtual assistants with LLMs (Large Language Models) and RAG (Retrieval Augmented Generation) with proprietary knowledge bases and document stores. Implement (Deep) Reinforcement Learning for autonomous control and operations. Build multi-agent collaborative systems with specialized AI agents – Perception, Reasoning, Planning, Action, and Learning Leverage AI or hybrid models to make intelligent recommendations and automate decision-making. Ensure AI models are scalable, deployable, and trustworthy 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. Requirements Bachelor's in computer science, AI, Machine Learning, or related field. At least 3 years of experience in applying AI/ML in asset-intensive industries such as oil & gas, utilities, or mining. Strong experience with Predictive Analytics and Prescriptive Analytics using tools like TensorFlow, PyTorch, and Keras. Proven experience in Generative AI – Meta LLaMA, OpenAI GPT, Anthropic Claude, etc., and RAG & vector embeddings for optimized knowledge retrieval and decision-making, and multi-agent systems for industrial applications. Experience in cloud-based AI deployments (AWS, Azure, or Google Cloud) and edge AI for real-time decision-making. Experience with SCADA, DCS, PLCs, smart sensors, IoT platforms, and Historians. Strong analytical, problem-solving, and communication skills, with a proven ability to work across teams Experience in Reliability Analytics and Condition-Based Maintenance (CBM) Knowledge of neural networks, deep reinforcement learning, and large language models Familiarity with multi-agent systems for collaborative decision-making and goal-seeking AI agents 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.