Senior AI architect designing generative and agentic AI solutions for GE Vernova’s grid automation business. Leading cloud, edge, MLOps, and production deployment architecture.
About the role
- Senior AI architect designing generative and agentic AI solutions for GE Vernova’s grid automation business. Leading cloud, edge, and production deployment architecture.
Responsibilities
- Design and architect scalable AI/ML solutions, including generative AI and agentic AI applications, for grid automation, digitalization, product-line use cases, and business efficiency
- Establish architectural standards, best practices, and technical guidelines for AI/ML development across the CTO organization
- Build technical foundations using modular and microservices architectures, cloud and edge patterns, API-first and privacy-by-design principles, containerization, orchestration, autoscaling, infrastructure as code, CI/CD, data and MLOps pipelines, and collaborative experimentation environments
- Design and deploy solutions on GE GridNode and other edge platforms using container and microservices principles
- Develop strategies to optimize model performance in production
- Advise project and product teams on architecture and solution approaches for generative AI and agentic AI use cases
- Define and implement monitoring, observability, and guardrails for generative AI and agentic AI solutions
- Guide teams in building production-quality systems and reusable frameworks that are scalable, reliable, and maintainable
- Collaborate with cross-functional teams to integrate AI/ML capabilities into existing platforms and develop new intelligent product and business-efficiency solutions
- Monitor advances in AI/ML, generative AI, agentic AI, and energy-system technologies and translate developments into practical recommendations
- Evaluate AI/ML tools, platforms, methodologies, and vendor solutions for grid-automation challenges and business opportunities
- Design and execute proofs of concept to validate feasibility and inform production decisions
- Report to the Director of Data Analytics and AI Solutions in the Grid Automation CTO organization
Requirements
- Bachelor's degree in Computer Science, Electrical Engineering, Data Science, or a related technical field
- Minimum of 7 years of hands-on experience in software engineering, AI/ML development, and/or solution architecture
- Demonstrated hands-on experience building and architecting generative AI and agentic AI solutions and applications
- Experience architecting and implementing generative AI and agentic AI solutions on AWS or comparable cloud infrastructure such as Microsoft Azure or Google Cloud Platform
- Proven expertise with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn
- Experience with generative AI technologies including large and small language models, diffusion models, and GANs
- Experience with agentic AI frameworks and architectures, including multi-agent orchestration, tool and function calling, retrieval-augmented generation, agent memory, and planning
- Experience implementing monitoring, observability, and guardrails for generative AI and agentic AI systems
- Experience applying AI/ML frameworks, AI/MLOps, and CI/CD across cloud-native and on-premises development and deployment in operational technology or industrial automation environments
- Experience developing and deploying ML models using cloud MLOps services such as Amazon SageMaker, Azure Machine Learning, Google Vertex AI, Dataiku Cloud, or equivalent
- Hands-on experience developing and testing AI/ML algorithms and experience with grid or physics models using MATLAB, PSCAD, PSS/E, DIgSILENT PowerFactory, or equivalent tools
- Experience with DevOps, data pipelines, model registries, Docker, and Kubernetes
- Experience designing solutions across the complete AI/ML lifecycle
- Strong background in edge computing, industrial IoT, real-time processing, and AWS, Microsoft Azure, or Google Cloud Platform
- Experience with graph databases, SQL and NoSQL databases, and Microsoft Access
- Proficiency in Python and at least one additional language such as C# or C++
- Experience in scientific programming or simulation tools such as MATLAB or R
- Experience with time-series analysis, signal processing, load forecasting, or predictive modeling relevant to energy systems and grid operations
- Proven record of delivering complex AI/ML projects from conception through production deployment
- Ability to apply research insights to real-world business problems and commercial solutions
- Understanding of industrial IoT, edge-computing requirements, and real-time data processing in critical-infrastructure environments
Benefits
- Discretionary annual bonus
- Relocation Assistance Provided: No
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