Job Description

GE Vernova is accelerating the path to more reliable, affordable, and sustainable energy, while helping our customers power economies and deliver the electricity that is vital to health, safety, security, and improved quality of life. Are you excited at the opportunity to electrify and decarbonize the world?

We are looking for an experienced, passionate, and results-oriented Machine Learning (ML) Engineer that will create and deliver innovative AI/ML solutions for Grid Automation (GA) products. This role requires substantial experience in developing, validating and deploying AI/ML models, typically gained through at least 5 years working experience in the energy, smart infrastructure, or industrial automation sectors. You will be actively involved in proof-of-concept projects as well as developing, validating and deploying models, ensuring solutions meet stringent accuracy, performance and operational standards both at the edge and in the cloud. The ideal candidate has a strong track record of independently leading and delivering AI/ML model projects in complex, data-rich environments, requiring someone who can drive innovation from concept to production.

This role will work collaboratively with multiple GA organizations such as the CTO AI/ML Team, product lines, R&D teams, and other business units to create innovative solutions for our customers and products. Your expertise in automation, AI, and their integration into these domains will be essential in shaping the company's mission to foster innovation and progress.

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Essential Responsibilities:

Lead the design, development, and deployment of scalable, high-performant, maintainable, and reliable ML and generative AI models for grid innovation applications within Grid Automation. Develop AI/ML applications for customer-driven use cases, including predictive maintenance, anomaly detection, failure analysis, optimized control and forecasting. Monitor, maintain, and optimize deployed AI/ML models to continuously enhance their accuracy and performance. Establish clear validation frameworks to ensure models meet required performance standards and business objectives. Design and conduct experiments to test and validate AI/ML models in the context of energy systems and grid automation applications (model selection, design, tuning, testing, refining, validation, optimization and deployment). Identify and address discrepancies between expected and actual model behavior, providing actionable insights to improve model performance. Establish test procedures to validate models with real and simulated grid data. Support the design, building and maintenance of MLOps pipelines in collaboration with team Architects, MLOps Engineers and other partners. Embrace MLOps principles to streamline the deployment and updating of ML models in production. Integrate AI/ML solutions effortlessly into grid automation systems, whether in the cloud or at the edge. Ensure that models are production-ready and continuously improve/evolve in line with emerging needs and technologies. Manage the collection, structuring, and analysis of data to enable seamless AI/ML applications. Ensure data adheres to data governance policies and industry standards. Collaborate closely with cross-functional teams to identify business challenges and deliver AI-driven solutions that are efficient, accurate, reliable, maintainable and scalable. Communicate validation results, insights, and recommendations clearly to stakeholders, including product managers and leadership teams. Build necessary understanding and expertise overtime to design and develop product features and applications such as Protection, Control, Monitoring and communication along with the AI/ML applications on the product

Must-Have Requirements

Master's or PhD in Data Science, Computer Science, Information Technology, Electrical Engineering, or a related field with hands-on experience as ML Engineer. Proven ML Engineer experience in the energy, smart infrastructure, or industrial automation sectors, with expertise in system protection, automation, monitoring, and diagnostics, typically acquired through a minimum of 5 years of service.

Solid experience developing and validating AI/ML models, ensuring they meet business and technical requirements.

Excellent foundation in AI/ML and statistical techniques, including supervised and unsupervised learning. Experience with deep learning algorithms, reinforcement learning, NLP, large language models (LLMs), small language models (SLMs) and computer vision. Experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn. Proficiency in programming languages such as Python, R, MATLAB, C# or C++. Hands-on, demonstrable experience deploying ML models in production environments using MLOps principles. Experience with time-series analysis, signal processing, load forecasting, optimization and predictive maintenance relevant to energy systems and grid operations. Experience with cloud platforms (e.g., AWS, Azure, GCP) and deployment of models in cloud environments. Familiarity with GraphDB, MongoDB, SQL/NoSQL, and other DBMS technologies. Strong knowledge of statistical techniques, model technologies, performance metrics, and validation methodologies for AI/ML models. Experience with data visualization tools such as Tableau, Power BI, or similar to effectively present validation results and insights. Excellent communication, organizational, documentation and problem-solving skills.

Nice-to-Have Requirements:

Familiarity with big data tools and technologies, such as Hadoop, Kafka, and Spark. Familiarity with data governance frameworks and validation standards in the energy sector. Experience with containerization (Docker, Kubernetes), and distributed computing (Spark, Scala). Understanding of system automation, protection, and diagnostics for power utilities and industrial customers.

Additional Information

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Relocation Assistance Provided:

Yes