Lead Machine Learning Engineer

# Job Description Summary

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 a passionate, creative, and results-oriented Machine Learning (ML) Engineer with substantial experience in the energy, smart infrastructure, or industrial automation sectors to join our AI & Grid Innovation team. In this role, you will be at the forefront of developing, deploying, and validating cutting-edge AI/ML models designed specifically for grid innovation applications. You will play a key role in building systems to test and verify proof-of-concepts, delivering AI-driven solutions both at the edge and in the cloud.

As a Lead ML Engineer within the CTO organization, this position offers the opportunity to work collaboratively with Grid Automation product lines, R&D teams, and other business units to create impactful, sustainable, and inclusive solutions across energy systems, smart infrastructure, and industrial automation. Your expertise in automation, AI, and their integration into these domains will be essential in shaping the company’s mission to foster innovation, inclusivity, and progress. #LI-ML2

# Job Description

Essential Responsibilities

- Lead the design, development, and deployment of scalable, high-performant, maintainable, accurate 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, as well as business efficiency.

- Monitor, maintain, and optimize deployed AI/ML models to continuously enhance their accuracy and performance.

- 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.

- Validate and verify AI/ML proof-of-concepts in real-world environments, ensuring they meet the diverse needs of our customers.

- Manage the collection, structuring, and analysis of data to enable seamless AI/ML applications.

- Ensure that models are production-ready and continuously improve/evolve in line with emerging needs and technologies.

- Collaborate closely with cross-functional teams to identify business challenges and deliver AI-driven solutions that are efficient, accurate, reliable, maintainable and scalable.

- Integrate AI/ML solutions effortlessly into grid automation systems, whether in the cloud or at the edge.

Required Qualifications

- Master’s or PhD in Computer Science, Information Technology, Electrical Engineering, or a related field.

Desired Characteristics

- Excellent foundation in AI/ML techniques, including supervised, unsupervised, and reinforcement learning, deep learning, and large language models (LLMs).

- Experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn.

- Proficiency in programming languages such as Python, R, MATLAB, C# or C++.

- Proven experience in applying AI/ML frameworks/workflows, AI/MLOps and CI/CD using cloud-native and on-prem development and deployment in OT/industrial automation environments.

- Hands-on, demonstrable experience deploying ML models in production environments using MLOps principles.

- Proven experience in the energy, smart infrastructure, or industrial automation sectors, with expertise in system protection, automation, monitoring, and diagnostics.

- Experience with time-series analysis, signal processing, load forecasting, optimization and predictive maintenance relevant to energy systems and grid operations.

- Familiarity with cloud platforms (AWS, Azure, Google Cloud) and microservices architecture.

- Familiarity with GraphDB, MongoDB, SQL/NoSQL, and other DBMS technologies.

- Excellent communication, organizational, documentation and problem-solving skills.

- Strong emphasis on teamwork, having a can-do attitude, problem solving, positivity, collaboration, and fostering inclusive environments.

- Ability to multi-task in a fast-paced, multi-task rich environment.

- Experience with data modeling, containerization (Docker, Kubernetes), and distributed computing (Spark, Scala).

- Understanding of system automation, protection, and diagnostics for power utilities and industrial customers.

- Experience with deep learning algorithms, reinforcement learning, NLP, and computer vision in applicable domains.

# Additional Information

Relocation Assistance Provided: No