Machine Learning Engineer - AI for Grid Innovation & Energy Transition

Job Description Summary

We are looking for a passionate, creative, and results-driven Machine Learning (ML) Engineer with solid experience in the role, typically gained through a minimum of 5 years working within multinational manufacturing companies operating in the energy, smart infrastructure, or industrial automation sectors. The ideal candidate has a strong track record of independently leading and delivering ML projects.

As part of our AI & Grid Innovation team, you will be at the forefront of developing, deploying, and validating cutting-edge AI/ML models specifically designed for grid innovation applications. You will play a key role in designing and building systems to test and verify proof-of-concepts, delivering AI-driven solutions both at the edge and in the cloud.

This role reports to the AI Director, within the CTO organization, and offers a unique opportunity to collaborate closely with Grid Automation product lines, R&D teams, and other business units. Together, you will contribute to creating impactful, sustainable, and inclusive solutions across the domains of energy systems, smart infrastructure, and industrial automation.

• Lead the design, development, and deployment of scalable AI/ML models for grid innovation applications in the energy, smart infrastructure, or industrial automation sectors.
• Create innovative analytics to optimize grid system performance and product differentiation.
• Develop AI/ML applications for customer-driven use cases, including predictive maintenance and load forecasting.
• Validate and verify AI/ML proof-of-concepts in real-world environments, ensuring they meet the diverse needs of our customers.
• Monitor, maintain, and optimize deployed AI/ML models to continuously enhance their accuracy and performance.
• Manage the collection, structuring, and analysis of data to enable seamless AI/ML applications.
• Ensure that models are production-ready and continuously improve in line with emerging needs and technologies.
• Embrace MLOps principles to streamline the deployment and updating of ML models in production.
• Collaborate closely with cross-functional teams to identify business challenges and deliver AI-driven solutions that are efficient, equitable, and scalable.
• Integrate AI/ML solutions effortlessly into grid automation systems, whether in the cloud or at the edge.

• Master's or PhD in Computer Science, Information Technology, Electrical Engineering, or a related field.
• Proven experience in the energy, smart infrastructure, or industrial automation sectors, with deep expertise in system protection, automation, monitoring, and diagnostics, typically acquired through a minimum of 5 years within a multinational manufacturing company.
• Strong foundation in AI/ML techniques, including supervised, unsupervised, and reinforcement learning, deep learning, and large language models (LLMs).
• Experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Hands-on experience deploying ML models in production environments using MLOps principles.
• Expertise in relevant AI/ML applications, such as predictive maintenance, load forecasting, or optimization.
• Proficiency in programming languages such as Python, R, MATLAB, or C++.
• Familiarity with cloud platforms (AWS, Azure, Google Cloud) and microservices architecture.

• Experience with data modeling, containerization (Docker, Kubernetes), and distributed computing (Spark, Scala).
• Familiarity with GraphDB, MongoDB, SQL/NoSQL, and other DBMS technologies.
• Understanding of system automation, protection, and diagnostics in relevant sectors.
• Experience with deep learning algorithms, reinforcement learning, NLP, and computer vision in applicable domains.
• Excellent communication, organizational, and problem-solving skills, with a strong emphasis on teamwork, collaboration, and fostering inclusive environments.