AI Engineer

Responsibilities

• Develop and implement AI models for predictive & prescriptive analytics in industrial applications.
• Process Optimization, and Autonomous Operations in asset-intensive industries such as Oil & Gas, Mining, and Utilities.
• Build and integrate machine learning, deep learning, and generative AI models (LLMs, RAG, multi-agent systems).
• Work with industrial data platforms (SCADA, DCS, PLC, Historians, MES, ERP) to ensure seamless AI integration.
• Deploy AI solutions in cloud-based environments, ensuring scalability and reliability.
• Apply industrial AI ethics and best practices to ensure compliance and responsible AI usage.
• Collaborate with cross-functional teams to translate AI research into practical, high-impact solutions.

Requirements

• Bachelor’s/master’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, DeepSeek R1, Anthropic Claude, etc., and RAG & vector embeddings for optimized knowledge retrieval and decision-making, and multi-agent systems for industrial applications.
• Expertise 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.
• Familiarity with KPI monitoring, criticality and risk assessment, and asset strategy optimization.