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Mid-level ML role, metro location, and broad ML/MLOps requirements increase applicant competition.
Prefers industrial automation and power-plant domain knowledge, reducing cross-industry transferability.
Explicit 2–5 years plus required ML, MLOps and industrial domain skills enforce moderate shortlisting rigor.
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Develop AI/ML solutions to automate control logic, graphics, reporting, and workflows enhancing engineering productivity.
Create predictive tools and dashboards for data-driven decision-making, predictive maintenance, and anomaly detection in power plant operations.
Collaborate with engineers and operators to design AI tools that improve safety, compliance, and plant performance integration.
Bachelor's degree or equivalent in Engineering, AI, Data Science, Electrical/Computer Engineering or related field.
2-5 years experience in AI/ML model development, deployment, and optimization in real-world applications.
Proficiency in Python, TensorFlow/PyTorch, and MLOps.
Experience with power plant operations or control systems not strictly mandatory but strongly preferred.
Strong expertise with machine learning, deep learning, generative AI including large language models (LLMs) applied to automation and decision support.
Experience integrating AI into industrial environments, especially power plants, with knowledge of control theory and systems like Emerson Ovation DCS.
Proven ability to deliver AI solutions for predictive maintenance, anomaly detection, and process optimization using time-series data in operational settings.