Machine Learning Engineer

Job Description 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 world’s electricity network, helping to build the grid of the future powered by renewable energy … they’re all part of our company’s 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.

Job Description

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 model’s 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 Information

Relocation Assistance Provided: No