Machine Learning Engineer

Summary Many employers promise the chance to make a difference at GE Vernova you can change the world Bringing clean affordable power to the developing world decarbonizing the worlds electricity network helping to build the grid of the future powered by renewable energy xe2x80xa6 theyre all part of our companys strategy If you re passionate about applying AI and excited to tackle UN SDG-7 13 and Energy Transition challenges as well as motivated by the prospect of shaping the future of energy industry through innovation and new business models we encourage you to apply Join us in our journey to redefine what s possible with AI and make a lasting impact on the world of Energy We are seeking a dynamic forward-thinking and results-driven Machine Learning ML engineer who will work on building and deployment of grid innovation application AIML models Additionally will develop systems to validate and verify proof-of-concepts of the AIML application in grid space Reporting to AI leader in CTO organization the Machine Learning engineer will work in close collaboration with GA product lines R D teams product management and other GA functions This role will also be responsible to work with other functions across Grid Automation GA business to identify areas where the business can leverage data and artificial intelligence to drive efficiency increase customer satisfaction and develop POCs to solve critical problems for our customers build state-of-the-art models and deploy them on edge or cloud based systems ESSENTIAL RESPONSIBILITIES The ML engineer will be responsible for Demonstrate novel transformational applications analytics to drive innovation differentiation Define the framework to collect structure and use of databases for AI to extract value Develop AI ML application to build differentiated products and solutions with ability to work on customers value-driven applications analytics to drive innovations Design and deploy high-quality scalable and secure AI ML models and applications on edge or cloud using native or container or microservices principles Monitor maintain and optimize deployed AI ML models ensuring continuous improvements in models accuracy and performance Develop and implement strategies for optimizing the performance of machine learning models in production Ensure that AI ML solutions are scalable efficient and integrate seamlessly with existing systems and data infrastructure Collaborate with cross-functional teams of product management R D and other functions to understand their needs and develop innovative solutions QUALIFICATIONS REQUIREMENTS Masters PhD Degree in computer science Information technology IT or electrical engineering specifically in the computer and electric power engineering field with hands-on experience in data science and AIML model building 1 years of experience of working in professional working environment and knowledge of statistical techniques artificial intelligence AI and machine learning ML including unsupervised learning supervised learning reinforcement learning Deep learning and large language models LLMs Strong expertise in algorithms libraries e g scikit-learn machine learning frameworks e g TensorFlow PyTorch Scikit-learn and data processing tools Proven experience in applying AI ML frameworks workflows in the production environment Usage of MLOPs to streamline the process of taking updating ML models to production Able to share ideas and work well in a team environment proactive approach to tasks displaying initiative Flexible and adaptable open to change and modification of tasks working in multi-tasking environment DESIRED CHARACTERISTICS 1 years of industry experience Ability to simulate using scientific programming tools or languages such as C C or Python R MATLAB etc Experience of developing and deploying ML models such as predictive maintenance load forecasting etc for the power system domain Extensive knowledge of machine learning algorithms deep learning reinforcement learning NLP and computer vision Understanding of data structures data modeling and software architecture Experience with Linux virtualized system deployment using VM Hypervisor EsXi KVM Xen etc Dockers and related tools Experience with microservices architecture containerization technologies Docker Kubernetes and cloud computing platforms AWS Azure Google Cloud Understanding and usage of GraphDB MongoDB SQL NoSQL MS Access databases Understanding experience applying data analytics for Electrical Power System or industrial OT system Understanding of GPU Spark Scala for distributed computing Understanding related to power system protection and automation monitoring and diagnostics Strong communication skills and a proactive and open approach to conflict resolution Strong organizational skills self-motivated and self-directed Additional InformationRelocation Assistance Provided No