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Machine Learning Engineer – AI for Grid Innovation & Energy Transition
Ge Vernova
Energy tech co. that designs, builds, services gas, nuclear, hydro, steam & wind power systems globally.
Develop, deploy, and validate AI/ML models for grid innovation and energy transition.
Integrate AI/ML solutions effortlessly into grid automation systems, whether in the cloud or at the edge.
Validate and verify AI/ML proof-of-concepts in real-world environments, ensuring they meet the diverse needs of our customers.
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.
Develop AI/ML applications for customer-driven use cases, including predictive maintenance and load forecasting.
Lead the design, development, and deployment of scalable AI/ML models for grid innovation applications in the energy, smart infrastructure, or industrial automation sectors.
Monitor, maintain, and optimize deployed AI/ML models to continuously enhance their accuracy and performance.
Create innovative analytics to optimize grid system performance and product differentiation.
Collaborate closely with cross-functional teams to identify business challenges and deliver AI-driven solutions that are efficient, equitable, and scalable.
Manage the collection, structuring, and analysis of data to enable seamless AI/ML applications.
What you bring
tensorflow
pytorch
aws
docker
python
phd
Experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn.
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.
Master’s or PhD in Computer Science, Information Technology, Electrical Engineering, or a related field.
Familiarity with cloud platforms (AWS, Azure, Google Cloud) and microservices architecture.
Familiarity with GraphDB, MongoDB, SQL/NoSQL, and other DBMS technologies.
Hands-on experience deploying ML models in production environments using MLOps principles.
Strong foundation in AI/ML techniques, including supervised, unsupervised, and reinforcement learning, deep learning, and large language models (LLMs).
Proficiency in programming languages such as Python, R, MATLAB, or C++.
Experience with deep learning algorithms, reinforcement learning, NLP, and computer vision in applicable domains.
Experience with data modeling, containerization (Docker, Kubernetes), and distributed computing (Spark, Scala).
Excellent communication, organizational, and problem-solving skills, with a strong emphasis on teamwork, collaboration, and fostering inclusive environments.
Understanding of system automation, protection, and diagnostics in relevant sectors.
Expertise in relevant AI/ML applications, such as predictive maintenance, load forecasting, or optimization.
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