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

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