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Mid-level AI role, metro location, and generalist ML title increase applicant competition.
Core ML skills transfer across industries, but industrial control system experience increases domain specificity.
Explicit 2–5 years plus mandatory ML/LLM, Python, TensorFlow/PyTorch and MLOps requirements raise strictness.
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Develop AI/ML solutions that automate control logic, reporting, and workflows to boost productivity in power plant operations.
Build predictive tools using system data for decision-making, including predictive maintenance and anomaly detection.
Collaborate with engineers and operators to design secure, compliant AI tools integrating natural language processing and dashboards for improved safety and performance.
Bachelor’s degree or equivalent in Engineering, AI, Data Science, Electrical/Computer Engineering, or related field.
2–5 years of hands-on experience in AI/ML model development, deployment, and optimization with real-world impact.
Proficiency in Python and TensorFlow/PyTorch; experience with MLOps.
Work Experience Required: 2–5 years in AI/ML; domain experience in power plants or industrial automation is highly preferred.
Strong expertise in machine learning, deep learning, generative AI (including LLMs), with deployment experience in industrial environments.
Background in power plant operations and control theory (PID, MPC, fuzzy logic) to apply AI for optimization.
Experience integrating AI with control systems like Emerson Ovation DCS and knowledgeable in responsible AI practices for critical infrastructure.