Machine Learning (Engineers)

An AI / Machine Learning Engineer in the renewable energy sector develops intelligent systems that enhance efficiency, predict energy generation, and optimize maintenance. The role involves designing algorithms that interpret large-scale operational data from solar plants, wind farms, and energy grids to forecast performance, detect anomalies, and automate decision-making. This professional plays a key role in building the digital backbone for energy transition and smart infrastructure. Key Responsibilities: Design and implement ML models for predictive maintenance of solar inverters, trackers, and modules. Develop algorithms to forecast energy generation based on weather and irradiance data. Integrate AI-driven tools for fault detection, energy yield analysis, and performance optimization. Work with IoT and SCADA data to improve operational analytics. Collaborate with data scientists, energy engineers, and IT teams to deploy ML models in production. Evaluate model performance using KPIs like accuracy, recall, and F1 scores. Automate data processing pipelines using Python, TensorFlow, and cloud-based AI platforms. Stay updated with emerging trends in green AI, smart grid technologies, and digital twins. Required Skills & Qualifications: Bachelor?s or Master?s degree in Computer Science, Data Science, AI, or Electrical Engineering. Strong programming skills in Python, R, and SQL. Experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. Understanding of renewable energy systems, IoT sensors, and time-series data. Knowledge of cloud services (AWS, Azure, Google Cloud) for model deployment. Preferred Experience: 2?5 years of experience in AI or ML development, ideally in the energy, manufacturing, or IoT domain. Experience in building predictive models for asset performance or grid optimization. Career Growth & Opportunities: AI/ML Engineers can grow into roles like Senior Data Scientist, AI Architect, or Head of Energy Analytics. With the growing adoption of digitalization and smart grids, this role is among the most future-proof in the renewable industry. Professionals in this space will be driving automation and innovation for sustainable power solutions worldwide.