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 the GE GridNode/ edge platforms using container or microservices principles. Develop and implement strategies for optimizing the performance and scalability of machine learning models in production.
Collaborate with product management RD and other functions in to understand their needs and develop innovative solutions. Implement and maintain data pipelines for AI/ML models. Monitor and optimize the performance of AI/ML models in production.
Identification of Intellectual property/IP clearance.
Collaborate with cross-functional teams.
Qualification/Requirements:
Master/PhD Degree in computer science Information technology (IT) electrical engineering or electric power engineering specifically in the computer and electric power engineering field with minimum 6+ years of data science working experience.
6+ years of professional working environment and knowledge of artificial intelligence (AI) and machine learning (ML) including unsupervised learning supervised learning and reinforcement learning large language models (LLMs).
5-10 years RD or Applications experience related to power system protection and automation.
Proven experience in applying AI/ML frameworks/workflows AI/MLOps with CI/CD using Cloud-native and on-prem development and deployment in OT/industrial automation environments.
Hands-on professional experience in developing and testing AI/ML algorithms; AND/OR demonstrated professional experience with different scenarios of grid/physics models in power system simulation tools MATLAB/PSCAD; as well as dynamics PSS/E Digsilent and equivalent.
Experience with MLOps principles.
Experience with DevOps data pipelines Azure ML registry deployment methods viz. Docker K8s etc.
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.
Demonstrated professional experience with different scenarios of appropriate AI/ML models for energy/grid applications.
Desired Characteristics:
6+ years of research or industry experience with simulation using scientific programming tools or languages such as MATLAB C++ C# or Python R etc.
3 years of experience in developing and implementing ML models such as predictive maintenance load forecasting and grid optimization using cloud servers such as AWS Sagemaker or equivalent in the Power Systems domain.
2 years of experience in a MLOps data engineering and cloud working with real-time distribution grid data.
Experience with Linux virtualized system deployment using VM Hypervisor (EsXi KVM Xen etc. ) Dockers and related tools.
Experience as a system architect team lead industry recognized subject matter expert.
Advanced experience in utilizing and applying common programming languages such as Python C/C++ Java Spark and Hadoop R Programming Kafka C# MATLAB along with good familiarity with power system modelling and data communication format.